bucld_beh_analysis
Jojo Hu
10/17/2019
bucld Behavioral Analysis
Age at MRI and EEG
setwd("/Users/jojohu/Documents/Qlab/bucld")
drive_checklist <-
read.csv("/Users/jojohu/Documents/Qlab/bucld/drive_checklist.csv")
#Reformat date columns
drive_checklist$date_of_mri <-
as.Date(drive_checklist$date_of_mri, format = "%Y-%m-%d")
drive_checklist$date.of.birth <-
as.Date(drive_checklist$date.of.birth, format = "%Y-%m-%d")
drive_checklist$date_of_eeg <-
as.Date(drive_checklist$date_of_eeg, format = "%Y-%m-%d")
#Calculate age in days
for (i in 1:length(drive_checklist$date.of.birth)) {
if (!is.na(drive_checklist[i, "date_of_mri"])) {
drive_checklist[i, "age_at_neuro_day"] <-
difftime(drive_checklist[i, "date_of_mri"], drive_checklist[i, "date.of.birth"],
units = c("days"))
} else {
drive_checklist[i, "age_at_neuro_day"] <-
difftime(drive_checklist[i, "date_of_eeg"], drive_checklist[i, "date.of.birth"],
units = c("days"))
}
}
#Calculate age in months---------------------------------------------------------------------------------------------
#From Stackoverflow: https://stackoverflow.com/questions/1995933/number-of-months-between-two-dates
#Answer by https://stackoverflow.com/users/143305/dirk-eddelbuettel
#Credit to: Dirk Eddelbuettel
# turn a date into a 'monthnumber' relative to an origin
monnb <- function(d) {
lt <- as.POSIXlt(as.Date(d, origin="1900-01-01"))
lt$year*12 + lt$mon
}
# compute a month difference as a difference between two monnb's
mondf <- function(d1, d2) {
monnb(d2) - monnb(d1)
}
#Calculate age in months
for (i in 1:length(drive_checklist$date.of.birth)) {
if (!is.na(drive_checklist[i, "date_of_mri"])) {
drive_checklist[i, "age_at_neuro_month"] <-
mondf(drive_checklist[i, "date.of.birth"], drive_checklist[i, "date_of_mri"])
} else {
drive_checklist[i, "age_at_neuro_month"] <-
mondf(drive_checklist[i, "date.of.birth"], drive_checklist[i, "date_of_eeg"])
}
}
drive_checklist$age_at_neuro_year <- round(drive_checklist$age_at_neuro_month /12, 3)#Clean Data
## Warning: NAs introduced by coercionAll behavioral data prep
#Make all measures into wide form by participant and measure
library("reshape")
library("data.table")##
## Attaching package: 'data.table'## The following object is masked from 'package:reshape':
##
## meltlibrary("dplyr")##
## Attaching package: 'dplyr'## The following objects are masked from 'package:data.table':
##
## between, first, last## The following object is masked from 'package:reshape':
##
## rename## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionblast_spoli_data_wide <- cast(blast_spoli_data_long, part_id~measure+task)
#Extract relevant demo information
gender_group_info <- bucld_demo_all[,c("part_id", "group", "sex", "age_at_web_month", "age_at_web_year")]
#Merge all demo data with sl measures
blast_spoli_data_wide <-
merge(gender_group_info, blast_spoli_data_wide,
by.x = "part_id", by.y = "part_id",
all.y = TRUE)
blast_spoli_data_wide$accuracy_children_lsl_random_2afc_accuracies <-
as.numeric(as.character(blast_spoli_data_wide$accuracy_children_lsl_random_2afc_accuracies))
blast_spoli_data_wide$accuracy_lsl_predictable_2afc_accuracies <-
as.numeric(as.character(blast_spoli_data_wide$accuracy_lsl_predictable_2afc_accuracies))
blast_spoli_data_wide$entropy_children_lsl_predictable_entropy <-
as.numeric(as.character(blast_spoli_data_wide$entropy_children_lsl_predictable_entropy))
blast_spoli_data_wide$entropy_children_lsl_randomized_entropy <-
as.numeric(as.character(blast_spoli_data_wide$entropy_children_lsl_randomized_entropy))
blast_spoli_data_wide$accuracy_children_lsl_accuracies <-
coalesce(blast_spoli_data_wide$accuracy_children_lsl_random_2afc_accuracies,
blast_spoli_data_wide$accuracy_lsl_predictable_2afc_accuracies)
blast_spoli_data_wide$entropy_children_lsl_entropy <-
coalesce(blast_spoli_data_wide$entropy_children_lsl_predictable_entropy,
blast_spoli_data_wide$entropy_children_lsl_randomized_entropy)
ineligible_part_spoli <- blast_spoli_data_wide[which(blast_spoli_data_wide$age_at_web_year > 18),]
blast_spoli_data_wide <- blast_spoli_data_wide[-which(blast_spoli_data_wide$age_at_web_year > 18),] More Data Prep
Gender Chi-square
bucld_all_completed_gender_wide <- bucld_all_completed[,c("part_id", "group", "sex")]
bucld_all_completed_gender_long <- melt(bucld_all_completed_gender_wide, id.vars = c("part_id", "group"))
gender_wide <- cast(bucld_all_completed_gender_long, group~value, length)
# Chi-square on gender
# p<0.05 tells us that they are not matched for gender between group
print(gender_wide)## group F M
## 1 ASD 11 41
## 2 TD 13 8print(chisq.test(gender_wide))##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: gender_wide
## X-squared = 9.4859, df = 1, p-value = 0.002071Gender is NOT matched between group.
Gender Match Group, all analysis below changes
male_pool <- bucld_all_completed[which(bucld_all_completed$sex == "M" &
bucld_all_completed$group == "ASD"),]
other_data <- bucld_all_completed[-which(bucld_all_completed$sex == "M" &
bucld_all_completed$group == "ASD"),]
complete_count <-
apply(male_pool[c("accuracy_children_tsl_accuracies", "accuracy_children_ssl_accuracies",
"accuracy_children_vsl_accuracies", "accuracy_children_lsl_accuracies")], 1, function(x) {
sum(!is.na(x)) })
male_pool <- cbind(male_pool, complete_count)
male_pool <- male_pool[-which(male_pool$complete_count < 3),]
male_pool <- male_pool[ , !(names(male_pool) %in% "complete_count")]
sampled_male <- male_pool[sample(nrow(male_pool), 20), ]
bucld_all_completed <- rbind(other_data, sampled_male)
bucld_all_completed_gender_wide <- bucld_all_completed[,c("part_id", "group", "sex")]
bucld_all_completed_gender_long <- melt(bucld_all_completed_gender_wide, id.vars = c("part_id", "group"))
gender_wide <- cast(bucld_all_completed_gender_long, group~value, length)
# Chi-square on gender
# p<0.05 tells us that they are not matched for gender between group
print(gender_wide)## group F M
## 1 ASD 11 20
## 2 TD 13 8print(chisq.test(gender_wide))##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: gender_wide
## X-squared = 2.5337, df = 1, p-value = 0.1114Gender is now matched between group
Age difference between group
td_age <-
bucld_all_completed[which(bucld_all_completed$group == "TD"),
c("part_id", "group", "age_at_web_year")]
asd_age <-
bucld_all_completed[which(bucld_all_completed$group == "ASD"),
c("part_id", "group", "age_at_web_year")]
library("pastecs")##
## Attaching package: 'pastecs'## The following objects are masked from 'package:dplyr':
##
## first, last## The following objects are masked from 'package:data.table':
##
## first, lasttd_age_descrip_stat <- stat.desc(td_age)
td_age_descrip_stat <- td_age_descrip_stat[c("nbr.val","mean", "std.dev"),"age_at_web_year"]
td_age_descrip_stat <- data.frame(td_age_descrip_stat)
td_age_descrip_stat <- cbind(c("n","mean", "std.dev"), td_age_descrip_stat)
print(td_age_descrip_stat)## c("n", "mean", "std.dev") td_age_descrip_stat
## 1 n 21.000000
## 2 mean 9.290476
## 3 std.dev 2.086601asd_age_descrip_stat <- stat.desc(asd_age)
asd_age_descrip_stat <- asd_age_descrip_stat[c("nbr.val","mean", "std.dev"),"age_at_web_year"]
asd_age_descrip_stat <- data.frame(asd_age_descrip_stat)
asd_age_descrip_stat <- cbind(c("n","mean", "std.dev"), asd_age_descrip_stat)
print(asd_age_descrip_stat)## c("n", "mean", "std.dev") asd_age_descrip_stat
## 1 n 31.000000
## 2 mean 8.419355
## 3 std.dev 2.212151#Test age difference between group, p <0.05 means age is significantly different between group
print(t.test(td_age$age_at_web_year, asd_age$age_at_web_year))##
## Welch Two Sample t-test
##
## data: td_age$age_at_web_year and asd_age$age_at_web_year
## t = 1.4415, df = 44.754, p-value = 0.1564
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.3462009 2.0884436
## sample estimates:
## mean of x mean of y
## 9.290476 8.419355Age is NOT significantly different between TD and ASD (for gender-matched and all online participants.)
Prep data for graphs, remove outliers
bucld_sl_bar_all <- bucld_all_completed[,c(1:2, 6:10, 25, 11:15, 26, 17:20, 21:24)]
#Remove outliners for accuracy data
ssl_accurarcy_outlier <-
bucld_sl_bar_all[which(bucld_sl_bar_all$accuracy_children_ssl_accuracies < 0.15), "part_id"]
tsl_accurarcy_outlier <-
bucld_sl_bar_all[which(bucld_sl_bar_all$accuracy_children_tsl_accuracies < 0.15), "part_id"]
vsl_accurarcy_outlier <-
bucld_sl_bar_all[which(bucld_sl_bar_all$accuracy_children_vsl_accuracies < 0.15), "part_id"]
lsl_accurarcy_outlier <-
bucld_sl_bar_all[which(bucld_sl_bar_all$accuracy_children_lsl_accuracies < 0.15), "part_id"]
if(length(ssl_accurarcy_outlier) != 0) {
bucld_sl_bar_all[which(
bucld_sl_bar_all$part_id %in% ssl_accurarcy_outlier),
c("accuracy_children_ssl_accuracies")] <- NA
}
if(length(ssl_accurarcy_outlier) != 0) {
bucld_all_completed[which(
bucld_all_completed$part_id %in% ssl_accurarcy_outlier),
c("accuracy_children_ssl_accuracies")] <- NA
}
if(length(tsl_accurarcy_outlier) != 0) {
bucld_sl_bar_all[which(
bucld_sl_bar_all$part_id %in% tsl_accurarcy_outlier),
c("accuracy_children_tsl_accuracies")] <- NA
}
if(length(tsl_accurarcy_outlier) != 0) {
bucld_all_completed[which(
bucld_all_completed$part_id %in% tsl_accurarcy_outlier),
c("accuracy_children_tsl_accuracies")] <- NA
}
if(length(vsl_accurarcy_outlier) != 0) {
bucld_sl_bar_all[which(
bucld_sl_bar_all$part_id %in% vsl_accurarcy_outlier),
c("accuracy_children_vsl_accuracies")] <- NA
}
if(length(vsl_accurarcy_outlier) != 0) {
bucld_all_completed[which(
bucld_all_completed$part_id %in% vsl_accurarcy_outlier),
c("accuracy_children_vsl_accuracies")] <- NA
}
if(length(lsl_accurarcy_outlier) != 0) {
bucld_sl_bar_all[which(
bucld_sl_bar_all$part_id %in% lsl_accurarcy_outlier),
c("accuracy_children_lsl_accuracies")] <- NA
}
if(length(lsl_accurarcy_outlier) != 0) {
bucld_all_completed[which(
bucld_all_completed$part_id %in% lsl_accurarcy_outlier),
c("accuracy_children_lsl_accuracies")] <- NA
}
#Remove outliners for RT and RT slope data-----------------------------------------------------------------
ssl_rt_hit_count <- read.csv("ssl_rt_hit_trial_count.csv")
tsl_rt_hit_count <- read.csv("tsl_rt_hit_trial_count.csv")
vsl_rt_hit_count <- read.csv("vsl_rt_hit_trial_count.csv")
lsl_rt_hit_count <- read.csv("lsl_rt_hit_trial_count.csv")
ssl_rt_hit_count <-
ssl_rt_hit_count[which(ssl_rt_hit_count$part_id %in% bucld_sl_bar_all$part_id),]
tsl_rt_hit_count <-
tsl_rt_hit_count[which(tsl_rt_hit_count$part_id %in% bucld_sl_bar_all$part_id),]
vsl_rt_hit_count <-
vsl_rt_hit_count[which(vsl_rt_hit_count$part_id %in% bucld_sl_bar_all$part_id),]
lsl_rt_hit_count <-
lsl_rt_hit_count[which(lsl_rt_hit_count$part_id %in% bucld_sl_bar_all$part_id),]
ssl_rt_outlier <- ssl_rt_hit_count[which(ssl_rt_hit_count$hit_trial_number < 6), "part_id"]
tsl_rt_outlier <- tsl_rt_hit_count[which(tsl_rt_hit_count$hit_trial_number < 6), "part_id"]
vsl_rt_outlier <- vsl_rt_hit_count[which(vsl_rt_hit_count$hit_trial_number < 6), "part_id"]
lsl_rt_outlier <- lsl_rt_hit_count[which(lsl_rt_hit_count$hit_trial_number < 6), "part_id"]
if(length(ssl_rt_outlier) != 0) {
bucld_sl_bar_all[which(
bucld_sl_bar_all$part_id %in% ssl_rt_outlier),
c("rt_children_ssl_indiv_rts",
"slope_children_ssl_indiv_rts_slope")] <- NA
}
if(length(ssl_rt_outlier) != 0) {
bucld_all_completed[which(
bucld_all_completed$part_id %in% ssl_rt_outlier),
c("rt_children_ssl_indiv_rts",
"slope_children_ssl_indiv_rts_slope")] <- NA
}
if(length(tsl_rt_outlier) != 0) {
bucld_sl_bar_all[which(
bucld_sl_bar_all$part_id %in% tsl_rt_outlier),
c("rt_children_tsl_indiv_rts",
"slope_children_tsl_indiv_rts_slope")] <- NA
}
if(length(tsl_rt_outlier) != 0) {
bucld_all_completed[which(
bucld_all_completed$part_id %in% tsl_rt_outlier),
c("rt_children_tsl_indiv_rts",
"slope_children_tsl_indiv_rts_slope")] <- NA
}
if(length(vsl_rt_outlier) != 0) {
bucld_sl_bar_all[which(
bucld_sl_bar_all$part_id %in% vsl_rt_outlier),
c("rt_children_vsl_indiv_rts",
"slope_children_vsl_indiv_rts_slope")] <- NA
}
if(length(vsl_rt_outlier) != 0) {
bucld_all_completed[which(
bucld_all_completed$part_id %in% vsl_rt_outlier),
c("rt_children_vsl_indiv_rts",
"slope_children_vsl_indiv_rts_slope")] <- NA
}
if(length(lsl_rt_outlier) != 0) {
bucld_sl_bar_all[which(
bucld_sl_bar_all$part_id %in% lsl_rt_outlier),
c("rt_children_lsl_indiv_rts",
"slope_children_lsl_indiv_rts_slope")] <- NA
}
if(length(lsl_rt_outlier) != 0) {
bucld_all_completed[which(
bucld_all_completed$part_id %in% lsl_rt_outlier),
c("rt_children_lsl_indiv_rts",
"slope_children_lsl_indiv_rts_slope")] <- NA
}These are removed outliers:
SSL RT and Slope < 6 hits: blast_c_081
TSL RT and Slope < 6 hits: blast_c_081, blast_c_138, blast_c_209
VSL RT and Slope < 6 hits:
LSL RT and Slope < 6 hits:
SSL Accuracy:
TSL Accuracy:
VSL Accuracy:
LSL Accuracy:
Plot Accuracy line plot
Group SL analysis & KBIT, SCQ Descriptive Stat
T-test TD vs. ASD
td_sl <-
bucld_all_completed[which(bucld_all_completed$group == "TD"),]
asd_sl <-
bucld_all_completed[which(bucld_all_completed$group == "ASD"),]
library("pastecs")
td_sl$scq_web <- as.numeric(as.character(td_sl$scq_web))
td_kbit_scq_descrip_stat <- stat.desc(td_sl[,c(34:35, 27, 41)])## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning in qt((0.5 + p/2), (Nbrval - 1)): NaNs producedtd_kbit_scq_descrip_stat <- td_kbit_scq_descrip_stat[c("nbr.val","mean", "std.dev"),
c("kbit_matrices_raw", "kbit_matrices_std", "scq_total")]
print(td_kbit_scq_descrip_stat)## kbit_matrices_raw kbit_matrices_std scq_total
## nbr.val 21.000000 21.00000 19.000000
## mean 31.047619 112.19048 2.210526
## std.dev 6.822582 15.70229 1.583910asd_kbit_scq_descrip_stat <- stat.desc(asd_sl[,c(34:35, 27, 41)])
asd_kbit_scq_descrip_stat <- asd_kbit_scq_descrip_stat[c("nbr.val","mean", "std.dev"),
c("kbit_matrices_raw", "kbit_matrices_std", "scq_total" , "scq_web")]
print(asd_kbit_scq_descrip_stat)## kbit_matrices_raw kbit_matrices_std scq_total scq_web
## nbr.val 7.00000 7.00000 7.00000 27.000000
## mean 27.00000 108.28571 13.28571 19.444444
## std.dev 7.81025 17.97882 4.88925 8.576952#p <0.05, TD and ASD are truly different
#t.test(td_sl$accuracy_children_ssl_accuracies, asd_sl$accuracy_children_ssl_accuracies)
t_test_multi_pair <-
function(x,y){
test <- t.test(x,y)
data.frame(p_value = test$p.value,
df = test$parameter,
t_stat = test$statistic)
}
#Results withOUT outliers
sapply(intersect(colnames(td_sl),colnames(asd_sl))[c(6:10, 25)],
function(x) t_test_multi_pair(td_sl[,x], asd_sl[,x]))## accuracy_children_lsl_random_2afc_accuracies
## p_value 0.006701259
## df 18.04752
## t_stat 3.061861
## accuracy_children_ssl_accuracies accuracy_children_tsl_accuracies
## p_value 0.153828 0.3563519
## df 20.26406 27.89826
## t_stat 1.481609 0.9378867
## accuracy_children_vsl_accuracies
## p_value 0.3289073
## df 28.24031
## t_stat 0.9934916
## accuracy_lsl_predictable_2afc_accuracies
## p_value 0.1453067
## df 4.811187
## t_stat 1.736381
## accuracy_children_lsl_accuracies
## p_value 0.006187383
## df 27.00123
## t_stat 2.969725sapply(intersect(colnames(td_sl),colnames(asd_sl))[c(11:15, 26)],
function(x) t_test_multi_pair(td_sl[,x], asd_sl[,x]))## entropy_children_lsl_predictable_entropy
## p_value 0.5143953
## df 4.004192
## t_stat -0.7144384
## entropy_children_lsl_randomized_entropy
## p_value 0.3857383
## df 15.89571
## t_stat -0.8919208
## entropy_children_ssl_entropy entropy_children_tsl_entropy
## p_value 0.894859 0.6054487
## df 21.69667 19.29952
## t_stat -0.1337168 -0.5251667
## entropy_children_vsl_entropy entropy_children_lsl_entropy
## p_value 0.3407335 0.2143877
## df 28.57119 22.17205
## t_stat -0.9688849 -1.278232sapply(intersect(colnames(td_sl),colnames(asd_sl))[c(17:20)],
function(x) t_test_multi_pair(td_sl[,x], asd_sl[,x]))## rt_children_lsl_indiv_rts rt_children_ssl_indiv_rts
## p_value 0.5155238 0.4459984
## df 30.02629 28.05335
## t_stat -0.6580439 0.7729805
## rt_children_tsl_indiv_rts rt_children_vsl_indiv_rts
## p_value 0.0695137 0.4945933
## df 23.85312 29.98968
## t_stat 1.900543 -0.6914628sapply(intersect(colnames(td_sl),colnames(asd_sl))[c(21:24)],
function(x) t_test_multi_pair(td_sl[,x], asd_sl[,x]))## slope_children_lsl_indiv_rts_slope
## p_value 0.1501243
## df 23.19166
## t_stat -1.488377
## slope_children_ssl_indiv_rts_slope
## p_value 0.575198
## df 29.91358
## t_stat -0.5666224
## slope_children_tsl_indiv_rts_slope
## p_value 0.8804594
## df 33.63381
## t_stat 0.1515345
## slope_children_vsl_indiv_rts_slope
## p_value 0.07461568
## df 33.98648
## t_stat 1.839334# Still in lab sample comparison for Kbit and SCQ:
sapply(intersect(colnames(td_sl),colnames(asd_sl))[c(34:35, 41)],
function(x) t_test_multi_pair(td_sl[,x], asd_sl[,x]))## kbit_matrices_raw kbit_matrices_std scq_total
## p_value 0.251086 0.6199097 0.0008166487
## df 9.260746 9.25946 6.469729
## t_stat 1.224258 0.5130836 -5.880579LSL accuracy significantly higher (p = 0.031) (TD > ASD). No outliners in the gender matched online sample.
SCQ In Lab significantly higher in ASD (DF = 4.22). No group difference is seen for KBIT (DF = 6.54).
Data Prep for group:task interaction analysis
Add modality and domain category to long-format data
Mixed level modeling
Data Prep for LMER
Run LMER for accuracy
# Order matters, task factor order matters in LMER
# https://stats.stackexchange.com/questions/14522/variable-order-and-accounted-variability-in-linear-mixed-effects-modeling
library(lmerTest)## Loading required package: lme4## Loading required package: Matrix##
## Attaching package: 'Matrix'## The following object is masked from 'package:reshape':
##
## expand##
## Attaching package: 'lmerTest'## The following object is masked from 'package:lme4':
##
## lmer## The following object is masked from 'package:stats':
##
## stepcrossed_random_effect_acc_long$task <-
factor(crossed_random_effect_acc_long$task,levels = c("accuracy_children_lsl_accuracies",
"accuracy_children_ssl_accuracies",
"accuracy_children_vsl_accuracies",
"accuracy_children_tsl_accuracies"))
crossed_random_effect_acc_long <-
crossed_random_effect_acc_long[order(crossed_random_effect_acc_long$task), ]
m1_acc = lmer(value~group*task + (1+task|part_id),
data=crossed_random_effect_acc_long,
control=lmerControl(optimizer = "bobyqa",
check.nobs.vs.nRE="ignore"))## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: large eigenvalue ratio
## - Rescale variables?## Warning: Model failed to converge with 1 negative eigenvalue: -8.6e-07summary(m1_acc)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: value ~ group * task + (1 + task | part_id)
## Data: crossed_random_effect_acc_long
## Control: lmerControl(optimizer = "bobyqa", check.nobs.vs.nRE = "ignore")
##
## REML criterion at convergence: 468.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.62080 -0.43100 -0.03051 0.36904 2.07541
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## part_id (Intercept) 0.4817 0.6941
## taskaccuracy_children_ssl_accuracies 0.5635 0.7507 -0.42
## taskaccuracy_children_vsl_accuracies 0.1409 0.3754 0.02
## taskaccuracy_children_tsl_accuracies 0.8577 0.9261 -0.54
## Residual 0.3594 0.5995
##
##
##
## -0.38
## 0.26 0.33
##
## Number of obs: 174, groups: part_id, 52
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) -0.3724 0.1742 41.2975
## groupTD 0.9302 0.2874 42.8184
## taskaccuracy_children_ssl_accuracies 0.2013 0.2148 40.8992
## taskaccuracy_children_vsl_accuracies 0.2081 0.1787 39.5986
## taskaccuracy_children_tsl_accuracies 0.2514 0.2395 44.9941
## groupTD:taskaccuracy_children_ssl_accuracies -0.3784 0.3728 46.0152
## groupTD:taskaccuracy_children_vsl_accuracies -0.5694 0.3013 42.6618
## groupTD:taskaccuracy_children_tsl_accuracies -0.6085 0.3982 45.4347
## t value Pr(>|t|)
## (Intercept) -2.138 0.03850 *
## groupTD 3.236 0.00234 **
## taskaccuracy_children_ssl_accuracies 0.937 0.35418
## taskaccuracy_children_vsl_accuracies 1.164 0.25130
## taskaccuracy_children_tsl_accuracies 1.050 0.29944
## groupTD:taskaccuracy_children_ssl_accuracies -1.015 0.31530
## groupTD:taskaccuracy_children_vsl_accuracies -1.890 0.06560 .
## groupTD:taskaccuracy_children_tsl_accuracies -1.528 0.13341
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gropTD tskccrcy_chldrn_s_
## groupTD -0.606
## tskccrcy_chldrn_s_ -0.579 0.351
## tskccrcy_chldrn_v_ -0.441 0.267 0.267
## tskccrcy_chldrn_t_ -0.626 0.379 0.395
## grpTD:tskccrcy_chldrn_s_ 0.334 -0.565 -0.576
## grpTD:tskccrcy_chldrn_v_ 0.261 -0.475 -0.159
## grpTD:tskccrcy_chldrn_t_ 0.376 -0.629 -0.237
## tskccrcy_chldrn_v_ tskccrcy_chldrn_t_
## groupTD
## tskccrcy_chldrn_s_
## tskccrcy_chldrn_v_
## tskccrcy_chldrn_t_ 0.410
## grpTD:tskccrcy_chldrn_s_ -0.154 -0.227
## grpTD:tskccrcy_chldrn_v_ -0.593 -0.243
## grpTD:tskccrcy_chldrn_t_ -0.247 -0.601
## grpTD:tskccrcy_chldrn_s_ grpTD:tskccrcy_chldrn_v_
## groupTD
## tskccrcy_chldrn_s_
## tskccrcy_chldrn_v_
## tskccrcy_chldrn_t_
## grpTD:tskccrcy_chldrn_s_
## grpTD:tskccrcy_chldrn_v_ 0.284
## grpTD:tskccrcy_chldrn_t_ 0.384 0.428
## convergence code: 0
## Model is nearly unidentifiable: large eigenvalue ratio
## - Rescale variables?m2_acc <- lmer(value~group:task + group + (1 | part_id), data = crossed_random_effect_acc_long)
summary(m2_acc)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: value ~ group:task + group + (1 | part_id)
## Data: crossed_random_effect_acc_long
##
## REML criterion at convergence: 476.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.98610 -0.57429 -0.08989 0.43375 2.73311
##
## Random effects:
## Groups Name Variance Std.Dev.
## part_id (Intercept) 0.2230 0.4722
## Residual 0.7261 0.8521
## Number of obs: 174, groups: part_id, 52
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) -0.3603 0.1864 151.4257
## groupTD 0.9203 0.3108 155.0828
## groupASD:taskaccuracy_children_ssl_accuracies 0.1807 0.2293 121.5254
## groupTD:taskaccuracy_children_ssl_accuracies -0.1776 0.3244 133.2601
## groupASD:taskaccuracy_children_vsl_accuracies 0.2069 0.2309 120.6146
## groupTD:taskaccuracy_children_vsl_accuracies -0.3692 0.3078 130.7347
## groupASD:taskaccuracy_children_tsl_accuracies 0.2411 0.2318 122.9002
## groupTD:taskaccuracy_children_tsl_accuracies -0.3847 0.3096 125.4984
## t value Pr(>|t|)
## (Intercept) -1.933 0.05513 .
## groupTD 2.961 0.00355 **
## groupASD:taskaccuracy_children_ssl_accuracies 0.788 0.43219
## groupTD:taskaccuracy_children_ssl_accuracies -0.547 0.58496
## groupASD:taskaccuracy_children_vsl_accuracies 0.896 0.37199
## groupTD:taskaccuracy_children_vsl_accuracies -1.199 0.23258
## groupASD:taskaccuracy_children_tsl_accuracies 1.040 0.30021
## groupTD:taskaccuracy_children_tsl_accuracies -1.243 0.21625
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gropTD grpASD:tskccrcy_chldrn_s_
## groupTD -0.600
## grpASD:tskccrcy_chldrn_s_ -0.641 0.384
## grpTD:tskccrcy_chldrn_s_ 0.000 -0.502 0.000
## grpASD:tskccrcy_chldrn_v_ -0.633 0.379 0.514
## grpTD:tskccrcy_chldrn_v_ 0.000 -0.531 0.000
## grpASD:tskccrcy_chldrn_t_ -0.635 0.381 0.513
## grpTD:tskccrcy_chldrn_t_ 0.000 -0.517 0.000
## grpTD:tskccrcy_chldrn_s_
## groupTD
## grpASD:tskccrcy_chldrn_s_
## grpTD:tskccrcy_chldrn_s_
## grpASD:tskccrcy_chldrn_v_ 0.000
## grpTD:tskccrcy_chldrn_v_ 0.512
## grpASD:tskccrcy_chldrn_t_ 0.000
## grpTD:tskccrcy_chldrn_t_ 0.490
## grpASD:tskccrcy_chldrn_v_
## groupTD
## grpASD:tskccrcy_chldrn_s_
## grpTD:tskccrcy_chldrn_s_
## grpASD:tskccrcy_chldrn_v_
## grpTD:tskccrcy_chldrn_v_ 0.000
## grpASD:tskccrcy_chldrn_t_ 0.506
## grpTD:tskccrcy_chldrn_t_ 0.000
## grpTD:tskccrcy_chldrn_v_
## groupTD
## grpASD:tskccrcy_chldrn_s_
## grpTD:tskccrcy_chldrn_s_
## grpASD:tskccrcy_chldrn_v_
## grpTD:tskccrcy_chldrn_v_
## grpASD:tskccrcy_chldrn_t_ 0.000
## grpTD:tskccrcy_chldrn_t_ 0.523
## grpASD:tskccrcy_chldrn_t_
## groupTD
## grpASD:tskccrcy_chldrn_s_
## grpTD:tskccrcy_chldrn_s_
## grpASD:tskccrcy_chldrn_v_
## grpTD:tskccrcy_chldrn_v_
## grpASD:tskccrcy_chldrn_t_
## grpTD:tskccrcy_chldrn_t_ 0.000anova(m1_acc, m2_acc)## refitting model(s) with ML (instead of REML)## Data: crossed_random_effect_acc_long
## Models:
## m2_acc: value ~ group:task + group + (1 | part_id)
## m1_acc: value ~ group * task + (1 + task | part_id)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## m2_acc 10 485.93 517.53 -232.97 465.93
## m1_acc 19 495.22 555.25 -228.61 457.22 8.7102 9 0.4644#write.csv(crossed_random_effect_acc_long, "bucld_accuracy_use_this_data.csv")Marginal main effect of group
No siginificant differences between models.
lmer_acc_dom_mod <- add_dom_mod_func(crossed_random_effect_acc_long)
library(lmerTest)
# lmer_acc_dom_mod$task <-
# factor(lmer_acc_dom_mod$task,levels = c("accuracy_children_lsl_accuracies",
# "accuracy_children_ssl_accuracies",
# "accuracy_children_vsl_accuracies",
# "accuracy_children_tsl_accuracies"))
#
# lmer_acc_dom_mod <-
# lmer_acc_dom_mod[order(lmer_acc_dom_mod$task), ]
m1_acc_mod = lmer(value~group*domain + (1+domain|part_id), data=lmer_acc_dom_mod,
control=lmerControl(optimizer = "bobyqa", check.nobs.vs.nRE="ignore"))
summary(m1_acc_mod)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: value ~ group * domain + (1 + domain | part_id)
## Data: lmer_acc_dom_mod
## Control: lmerControl(optimizer = "bobyqa", check.nobs.vs.nRE = "ignore")
##
## REML criterion at convergence: 473.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.96576 -0.57333 -0.07259 0.46667 2.67687
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## part_id (Intercept) 0.2437 0.4937
## domainnonling 0.1276 0.3572 -0.22
## Residual 0.6671 0.8168
## Number of obs: 174, groups: part_id, 52
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.2636 0.1419 43.3730 -1.857 0.07005 .
## groupTD 0.7342 0.2387 48.3798 3.076 0.00345 **
## domainnonling 0.1233 0.1684 45.5695 0.732 0.46793
## groupTD:domainnonling -0.4071 0.2835 50.4710 -1.436 0.15715
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gropTD dmnnnl
## groupTD -0.595
## domainnnlng -0.562 0.335
## grpTD:dmnnn 0.334 -0.589 -0.594m2_acc_mod <- lmer(value~group*domain + (1 | part_id), data = lmer_acc_dom_mod)
summary(m2_acc_mod)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: value ~ group * domain + (1 | part_id)
## Data: lmer_acc_dom_mod
##
## REML criterion at convergence: 474.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.0276 -0.5667 -0.1055 0.4692 2.6454
##
## Random effects:
## Groups Name Variance Std.Dev.
## part_id (Intercept) 0.2257 0.4751
## Residual 0.7093 0.8422
## Number of obs: 174, groups: part_id, 52
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.2665 0.1424 89.1491 -1.872 0.06446 .
## groupTD 0.7414 0.2394 101.5417 3.097 0.00253 **
## domainnonling 0.1304 0.1599 124.6399 0.815 0.41646
## groupTD:domainnonling -0.4229 0.2703 129.7045 -1.565 0.12007
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gropTD dmnnnl
## groupTD -0.595
## domainnnlng -0.564 0.336
## grpTD:dmnnn 0.334 -0.592 -0.592anova(m1_acc_mod, m2_acc_mod)## refitting model(s) with ML (instead of REML)## Data: lmer_acc_dom_mod
## Models:
## m2_acc_mod: value ~ group * domain + (1 | part_id)
## m1_acc_mod: value ~ group * domain + (1 + domain | part_id)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## m2_acc_mod 6 478.92 497.88 -233.46 466.92
## m1_acc_mod 8 482.60 507.87 -233.30 466.60 0.3242 2 0.8504Run LMER for entropy
crossed_random_effect_entropy_long$task <-
factor(crossed_random_effect_entropy_long$task,levels = c("entropy_children_lsl_entropy",
"entropy_children_ssl_entropy",
"entropy_children_vsl_entropy",
"entropy_children_tsl_entropy"))
m1_ent = lmer(value~group:task + group + (1+task|part_id),
data=crossed_random_effect_entropy_long,
control=lmerControl(optimizer = "bobyqa",
check.nobs.vs.nRE="ignore"))## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: large eigenvalue ratio
## - Rescale variables?## Warning: Model failed to converge with 1 negative eigenvalue: -4.0e-06summary(m1_ent)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: value ~ group:task + group + (1 + task | part_id)
## Data: crossed_random_effect_entropy_long
## Control: lmerControl(optimizer = "bobyqa", check.nobs.vs.nRE = "ignore")
##
## REML criterion at convergence: 475.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.4636 -0.3225 0.1155 0.4369 1.3351
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## part_id (Intercept) 0.6563 0.8101
## taskentropy_children_ssl_entropy 1.2418 1.1144 -0.65
## taskentropy_children_vsl_entropy 0.4653 0.6821 -0.43 0.68
## taskentropy_children_tsl_entropy 0.8385 0.9157 -0.55 0.72
## Residual 0.3405 0.5835
##
##
##
##
## 0.02
##
## Number of obs: 174, groups: part_id, 52
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 0.18798 0.19024 40.10805
## groupTD -0.45662 0.31574 41.06375
## groupASD:taskentropy_children_ssl_entropy -0.14438 0.26067 40.48315
## groupTD:taskentropy_children_ssl_entropy 0.19680 0.36070 48.25438
## groupASD:taskentropy_children_vsl_entropy -0.06583 0.20513 41.36809
## groupTD:taskentropy_children_vsl_entropy 0.06107 0.27794 46.54814
## groupASD:taskentropy_children_tsl_entropy -0.14460 0.23520 44.44044
## groupTD:taskentropy_children_tsl_entropy 0.15879 0.31007 46.48537
## t value Pr(>|t|)
## (Intercept) 0.988 0.329
## groupTD -1.446 0.156
## groupASD:taskentropy_children_ssl_entropy -0.554 0.583
## groupTD:taskentropy_children_ssl_entropy 0.546 0.588
## groupASD:taskentropy_children_vsl_entropy -0.321 0.750
## groupTD:taskentropy_children_vsl_entropy 0.220 0.827
## groupASD:taskentropy_children_tsl_entropy -0.615 0.542
## groupTD:taskentropy_children_tsl_entropy 0.512 0.611
##
## Correlation of Fixed Effects:
## (Intr) gropTD grpASD:tskntrpy_chldrn_s_
## groupTD -0.603
## grpASD:tskntrpy_chldrn_s_ -0.685 0.412
## grpTD:tskntrpy_chldrn_s_ 0.000 -0.537 0.000
## grpASD:tskntrpy_chldrn_v_ -0.559 0.337 0.582
## grpTD:tskntrpy_chldrn_v_ 0.000 -0.480 0.000
## grpASD:tskntrpy_chldrn_t_ -0.625 0.377 0.628
## grpTD:tskntrpy_chldrn_t_ 0.000 -0.499 0.000
## grpTD:tskntrpy_chldrn_s_
## groupTD
## grpASD:tskntrpy_chldrn_s_
## grpTD:tskntrpy_chldrn_s_
## grpASD:tskntrpy_chldrn_v_ 0.000
## grpTD:tskntrpy_chldrn_v_ 0.587
## grpASD:tskntrpy_chldrn_t_ 0.000
## grpTD:tskntrpy_chldrn_t_ 0.594
## grpASD:tskntrpy_chldrn_v_
## groupTD
## grpASD:tskntrpy_chldrn_s_
## grpTD:tskntrpy_chldrn_s_
## grpASD:tskntrpy_chldrn_v_
## grpTD:tskntrpy_chldrn_v_ 0.000
## grpASD:tskntrpy_chldrn_t_ 0.295
## grpTD:tskntrpy_chldrn_t_ 0.000
## grpTD:tskntrpy_chldrn_v_
## groupTD
## grpASD:tskntrpy_chldrn_s_
## grpTD:tskntrpy_chldrn_s_
## grpASD:tskntrpy_chldrn_v_
## grpTD:tskntrpy_chldrn_v_
## grpASD:tskntrpy_chldrn_t_ 0.000
## grpTD:tskntrpy_chldrn_t_ 0.322
## grpASD:tskntrpy_chldrn_t_
## groupTD
## grpASD:tskntrpy_chldrn_s_
## grpTD:tskntrpy_chldrn_s_
## grpASD:tskntrpy_chldrn_v_
## grpTD:tskntrpy_chldrn_v_
## grpASD:tskntrpy_chldrn_t_
## grpTD:tskntrpy_chldrn_t_ 0.000
## convergence code: 0
## Model is nearly unidentifiable: large eigenvalue ratio
## - Rescale variables?m2_ent <- lmer(value~group:task + group + (1 | part_id), data = crossed_random_effect_entropy_long)
summary(m2_ent)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: value ~ group:task + group + (1 | part_id)
## Data: crossed_random_effect_entropy_long
##
## REML criterion at convergence: 486
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1355 -0.3430 0.2051 0.5820 1.6927
##
## Random effects:
## Groups Name Variance Std.Dev.
## part_id (Intercept) 0.2319 0.4816
## Residual 0.7706 0.8778
## Number of obs: 174, groups: part_id, 52
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 0.16172 0.19162 151.99008
## groupTD -0.43684 0.31955 155.51632
## groupASD:taskentropy_children_ssl_entropy -0.13462 0.23621 121.95264
## groupTD:taskentropy_children_ssl_entropy 0.23361 0.33412 133.62360
## groupASD:taskentropy_children_vsl_entropy -0.03138 0.23786 121.04237
## groupTD:taskentropy_children_vsl_entropy 0.08322 0.31704 131.10348
## groupASD:taskentropy_children_tsl_entropy -0.09215 0.23872 123.31929
## groupTD:taskentropy_children_tsl_entropy 0.15771 0.31885 125.88148
## t value Pr(>|t|)
## (Intercept) 0.844 0.400
## groupTD -1.367 0.174
## groupASD:taskentropy_children_ssl_entropy -0.570 0.570
## groupTD:taskentropy_children_ssl_entropy 0.699 0.486
## groupASD:taskentropy_children_vsl_entropy -0.132 0.895
## groupTD:taskentropy_children_vsl_entropy 0.263 0.793
## groupASD:taskentropy_children_tsl_entropy -0.386 0.700
## groupTD:taskentropy_children_tsl_entropy 0.495 0.622
##
## Correlation of Fixed Effects:
## (Intr) gropTD grpASD:tskntrpy_chldrn_s_
## groupTD -0.600
## grpASD:tskntrpy_chldrn_s_ -0.642 0.385
## grpTD:tskntrpy_chldrn_s_ 0.000 -0.503 0.000
## grpASD:tskntrpy_chldrn_v_ -0.634 0.380 0.514
## grpTD:tskntrpy_chldrn_v_ 0.000 -0.531 0.000
## grpASD:tskntrpy_chldrn_t_ -0.637 0.382 0.513
## grpTD:tskntrpy_chldrn_t_ 0.000 -0.517 0.000
## grpTD:tskntrpy_chldrn_s_
## groupTD
## grpASD:tskntrpy_chldrn_s_
## grpTD:tskntrpy_chldrn_s_
## grpASD:tskntrpy_chldrn_v_ 0.000
## grpTD:tskntrpy_chldrn_v_ 0.512
## grpASD:tskntrpy_chldrn_t_ 0.000
## grpTD:tskntrpy_chldrn_t_ 0.491
## grpASD:tskntrpy_chldrn_v_
## groupTD
## grpASD:tskntrpy_chldrn_s_
## grpTD:tskntrpy_chldrn_s_
## grpASD:tskntrpy_chldrn_v_
## grpTD:tskntrpy_chldrn_v_ 0.000
## grpASD:tskntrpy_chldrn_t_ 0.506
## grpTD:tskntrpy_chldrn_t_ 0.000
## grpTD:tskntrpy_chldrn_v_
## groupTD
## grpASD:tskntrpy_chldrn_s_
## grpTD:tskntrpy_chldrn_s_
## grpASD:tskntrpy_chldrn_v_
## grpTD:tskntrpy_chldrn_v_
## grpASD:tskntrpy_chldrn_t_ 0.000
## grpTD:tskntrpy_chldrn_t_ 0.523
## grpASD:tskntrpy_chldrn_t_
## groupTD
## grpASD:tskntrpy_chldrn_s_
## grpTD:tskntrpy_chldrn_s_
## grpASD:tskntrpy_chldrn_v_
## grpTD:tskntrpy_chldrn_v_
## grpASD:tskntrpy_chldrn_t_
## grpTD:tskntrpy_chldrn_t_ 0.000anova(m1_ent, m2_ent)## refitting model(s) with ML (instead of REML)## Data: crossed_random_effect_entropy_long
## Models:
## m2_ent: value ~ group:task + group + (1 | part_id)
## m1_ent: value ~ group:task + group + (1 + task | part_id)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## m2_ent 10 495.77 527.36 -237.89 475.77
## m1_ent 19 502.93 562.95 -232.47 464.93 10.84 9 0.2868Run LMER for mean rt
m1_rt = lmer(value~group:task + group + (1+task|part_id),
data=crossed_random_effect_rt_long,
control=lmerControl(optimizer = "bobyqa",
check.nobs.vs.nRE="ignore"))## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: large eigenvalue ratio
## - Rescale variables?summary(m1_rt)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: value ~ group:task + group + (1 + task | part_id)
## Data: crossed_random_effect_rt_long
## Control: lmerControl(optimizer = "bobyqa", check.nobs.vs.nRE = "ignore")
##
## REML criterion at convergence: 463.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.5851 -0.4264 0.0149 0.4241 1.5301
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## part_id (Intercept) 0.6422 0.8014
## taskrt_children_ssl_indiv_rts 1.8238 1.3505 -0.84
## taskrt_children_tsl_indiv_rts 1.2626 1.1236 -0.76 0.48
## taskrt_children_vsl_indiv_rts 0.6539 0.8086 -0.50 0.63
## Residual 0.3683 0.6069
##
##
##
##
## 0.71
##
## Number of obs: 169, groups: part_id, 50
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 0.08056 0.19204 41.03480
## groupTD -0.14832 0.31920 41.85871
## groupASD:taskrt_children_ssl_indiv_rts -0.15328 0.30378 41.70050
## groupTD:taskrt_children_ssl_indiv_rts 0.23084 0.40468 48.44549
## groupASD:taskrt_children_tsl_indiv_rts -0.31196 0.27266 40.33388
## groupTD:taskrt_children_tsl_indiv_rts 0.46181 0.36216 43.67815
## groupASD:taskrt_children_vsl_indiv_rts -0.02088 0.22574 38.79820
## groupTD:taskrt_children_vsl_indiv_rts -0.07518 0.30106 45.14488
## t value Pr(>|t|)
## (Intercept) 0.420 0.677
## groupTD -0.465 0.645
## groupASD:taskrt_children_ssl_indiv_rts -0.505 0.617
## groupTD:taskrt_children_ssl_indiv_rts 0.570 0.571
## groupASD:taskrt_children_tsl_indiv_rts -1.144 0.259
## groupTD:taskrt_children_tsl_indiv_rts 1.275 0.209
## groupASD:taskrt_children_vsl_indiv_rts -0.092 0.927
## groupTD:taskrt_children_vsl_indiv_rts -0.250 0.804
##
## Correlation of Fixed Effects:
## (Intr) gropTD grpASD:tskrt_chldrn_s__
## groupTD -0.602
## grpASD:tskrt_chldrn_s__ -0.789 0.474
## grpTD:tskrt_chldrn_s__ 0.000 -0.615 0.000
## grpASD:tskrt_chldrn_t__ -0.736 0.443 0.483
## grpTD:tskrt_chldrn_t__ 0.000 -0.596 0.000
## grpASD:tskrt_chldrn_v__ -0.598 0.360 0.559
## grpTD:tskrt_chldrn_v__ 0.000 -0.505 0.000
## grpTD:tskrt_chldrn_s__ grpASD:tskrt_chldrn_t__
## groupTD
## grpASD:tskrt_chldrn_s__
## grpTD:tskrt_chldrn_s__
## grpASD:tskrt_chldrn_t__ 0.000
## grpTD:tskrt_chldrn_t__ 0.494 0.000
## grpASD:tskrt_chldrn_v__ 0.000 0.604
## grpTD:tskrt_chldrn_v__ 0.568 0.000
## grpTD:tskrt_chldrn_t__ grpASD:tskrt_chldrn_v__
## groupTD
## grpASD:tskrt_chldrn_s__
## grpTD:tskrt_chldrn_s__
## grpASD:tskrt_chldrn_t__
## grpTD:tskrt_chldrn_t__
## grpASD:tskrt_chldrn_v__ 0.000
## grpTD:tskrt_chldrn_v__ 0.613 0.000
## convergence code: 0
## Model is nearly unidentifiable: large eigenvalue ratio
## - Rescale variables?m2_rt <- lmer(value~task*group + (1 | part_id), data = crossed_random_effect_rt_long)
summary(m2_rt)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: value ~ task * group + (1 | part_id)
## Data: crossed_random_effect_rt_long
##
## REML criterion at convergence: 479.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.91925 -0.52512 0.00353 0.59411 2.63835
##
## Random effects:
## Groups Name Variance Std.Dev.
## part_id (Intercept) 0.04861 0.2205
## Residual 0.94317 0.9712
## Number of obs: 169, groups: part_id, 50
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 8.079e-02 1.916e-01 1.602e+02
## taskrt_children_ssl_indiv_rts -1.733e-01 2.648e-01 1.180e+02
## taskrt_children_tsl_indiv_rts -3.058e-01 2.672e-01 1.168e+02
## taskrt_children_vsl_indiv_rts 3.417e-04 2.623e-01 1.168e+02
## groupTD -2.130e-01 3.206e-01 1.604e+02
## taskrt_children_ssl_indiv_rts:groupTD 4.807e-01 4.493e-01 1.256e+02
## taskrt_children_tsl_indiv_rts:groupTD 8.494e-01 4.449e-01 1.204e+02
## taskrt_children_vsl_indiv_rts:groupTD -9.503e-03 4.338e-01 1.228e+02
## t value Pr(>|t|)
## (Intercept) 0.422 0.6739
## taskrt_children_ssl_indiv_rts -0.655 0.5140
## taskrt_children_tsl_indiv_rts -1.145 0.2547
## taskrt_children_vsl_indiv_rts 0.001 0.9990
## groupTD -0.664 0.5074
## taskrt_children_ssl_indiv_rts:groupTD 1.070 0.2867
## taskrt_children_tsl_indiv_rts:groupTD 1.909 0.0586 .
## taskrt_children_vsl_indiv_rts:groupTD -0.022 0.9826
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) tskrt_chldrn_s__ tskrt_chldrn_t__
## tskrt_chldrn_s__ -0.691
## tskrt_chldrn_t__ -0.683 0.495
## tskrt_chldrn_v__ -0.697 0.505 0.499
## groupTD -0.598 0.413 0.409
## tskrt_chldrn_s__:TD 0.407 -0.589 -0.291
## tskrt_chldrn_t__:TD 0.410 -0.297 -0.601
## tskrt_chldrn_v__:TD 0.422 -0.305 -0.302
## tskrt_chldrn_v__ gropTD tskrt_chldrn_s__:TD
## tskrt_chldrn_s__
## tskrt_chldrn_t__
## tskrt_chldrn_v__
## groupTD 0.417
## tskrt_chldrn_s__:TD -0.297 -0.684
## tskrt_chldrn_t__:TD -0.300 -0.689 0.491
## tskrt_chldrn_v__:TD -0.605 -0.709 0.507
## tskrt_chldrn_t__:TD
## tskrt_chldrn_s__
## tskrt_chldrn_t__
## tskrt_chldrn_v__
## groupTD
## tskrt_chldrn_s__:TD
## tskrt_chldrn_t__:TD
## tskrt_chldrn_v__:TD 0.509anova(m1_rt, m2_rt)## refitting model(s) with ML (instead of REML)## Data: crossed_random_effect_rt_long
## Models:
## m2_rt: value ~ task * group + (1 | part_id)
## m1_rt: value ~ group:task + group + (1 + task | part_id)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## m2_rt 10 489.50 520.80 -234.75 469.50
## m1_rt 19 491.05 550.51 -226.52 453.05 16.453 9 0.05799 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Run LMER for rt slope
m1_slope = lmer(rt_slope~group:task + group + (1+task|part_id),
data=bucld_all_completed_slope,
control=lmerControl(optimizer = "bobyqa",
check.nobs.vs.nRE="ignore"))## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl =
## control$checkConv, : unable to evaluate scaled gradient## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl =
## control$checkConv, : Model failed to converge: degenerate Hessian with 1
## negative eigenvalues## Warning: Model failed to converge with 1 negative eigenvalue: -2.0e-06summary(m1_slope)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: rt_slope ~ group:task + group + (1 + task | part_id)
## Data: bucld_all_completed_slope
## Control: lmerControl(optimizer = "bobyqa", check.nobs.vs.nRE = "ignore")
##
## REML criterion at convergence: -621.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6847 -0.3872 0.0265 0.4464 2.4481
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## part_id (Intercept) 0.0019277 0.04391
## taskslope_children_ssl_indiv_rts_slope 0.0022965 0.04792 -0.96
## taskslope_children_tsl_indiv_rts_slope 0.0019932 0.04464 -0.99
## taskslope_children_vsl_indiv_rts_slope 0.0023536 0.04851 -0.68
## Residual 0.0004418 0.02102
##
##
##
## 0.93
## 0.64 0.60
##
## Number of obs: 169, groups: part_id, 50
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 0.004309 0.009350
## groupTD -0.025104 0.015607
## groupASD:taskslope_children_ssl_indiv_rts_slope -0.013581 0.010811
## groupTD:taskslope_children_ssl_indiv_rts_slope 0.007214 0.014489
## groupASD:taskslope_children_tsl_indiv_rts_slope -0.005999 0.010330
## groupTD:taskslope_children_tsl_indiv_rts_slope 0.020242 0.013743
## groupASD:taskslope_children_vsl_indiv_rts_slope -0.013179 0.010929
## groupTD:taskslope_children_vsl_indiv_rts_slope 0.035408 0.014614
## df t value Pr(>|t|)
## (Intercept) 40.537953 0.461 0.6474
## groupTD 40.782043 -1.609 0.1154
## groupASD:taskslope_children_ssl_indiv_rts_slope 41.118220 -1.256 0.2161
## groupTD:taskslope_children_ssl_indiv_rts_slope 46.441406 0.498 0.6209
## groupASD:taskslope_children_tsl_indiv_rts_slope 40.553114 -0.581 0.5646
## groupTD:taskslope_children_tsl_indiv_rts_slope 42.925980 1.473 0.1481
## groupASD:taskslope_children_vsl_indiv_rts_slope 40.894940 -1.206 0.2348
## groupTD:taskslope_children_vsl_indiv_rts_slope 46.199084 2.423 0.0194
##
## (Intercept)
## groupTD
## groupASD:taskslope_children_ssl_indiv_rts_slope
## groupTD:taskslope_children_ssl_indiv_rts_slope
## groupASD:taskslope_children_tsl_indiv_rts_slope
## groupTD:taskslope_children_tsl_indiv_rts_slope
## groupASD:taskslope_children_vsl_indiv_rts_slope
## groupTD:taskslope_children_vsl_indiv_rts_slope *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gropTD grpASD:tskslp_chldrn_s___
## groupTD -0.599
## grpASD:tskslp_chldrn_s___ -0.898 0.538
## grpTD:tskslp_chldrn_s___ 0.000 -0.713 0.000
## grpASD:tskslp_chldrn_t___ -0.907 0.543 0.800
## grpTD:tskslp_chldrn_t___ 0.000 -0.728 0.000
## grpASD:tskslp_chldrn_v___ -0.698 0.418 0.612
## grpTD:tskslp_chldrn_v___ 0.000 -0.580 0.000
## grpTD:tskslp_chldrn_s___
## groupTD
## grpASD:tskslp_chldrn_s___
## grpTD:tskslp_chldrn_s___
## grpASD:tskslp_chldrn_t___ 0.000
## grpTD:tskslp_chldrn_t___ 0.798
## grpASD:tskslp_chldrn_v___ 0.000
## grpTD:tskslp_chldrn_v___ 0.631
## grpASD:tskslp_chldrn_t___
## groupTD
## grpASD:tskslp_chldrn_s___
## grpTD:tskslp_chldrn_s___
## grpASD:tskslp_chldrn_t___
## grpTD:tskslp_chldrn_t___ 0.000
## grpASD:tskslp_chldrn_v___ 0.580
## grpTD:tskslp_chldrn_v___ 0.000
## grpTD:tskslp_chldrn_t___
## groupTD
## grpASD:tskslp_chldrn_s___
## grpTD:tskslp_chldrn_s___
## grpASD:tskslp_chldrn_t___
## grpTD:tskslp_chldrn_t___
## grpASD:tskslp_chldrn_v___ 0.000
## grpTD:tskslp_chldrn_v___ 0.614
## grpASD:tskslp_chldrn_v___
## groupTD
## grpASD:tskslp_chldrn_s___
## grpTD:tskslp_chldrn_s___
## grpASD:tskslp_chldrn_t___
## grpTD:tskslp_chldrn_t___
## grpASD:tskslp_chldrn_v___
## grpTD:tskslp_chldrn_v___ 0.000
## convergence code: 0
## unable to evaluate scaled gradient
## Model failed to converge: degenerate Hessian with 1 negative eigenvaluesm2_slope <- lmer(rt_slope~group:task + group + (1 | part_id), data = bucld_all_completed_slope)
summary(m2_slope)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: rt_slope ~ group:task + group + (1 | part_id)
## Data: bucld_all_completed_slope
##
## REML criterion at convergence: -583.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.78214 -0.51160 0.01622 0.65299 2.37820
##
## Random effects:
## Groups Name Variance Std.Dev.
## part_id (Intercept) 2.212e-05 0.004703
## Residual 1.320e-03 0.036333
## Number of obs: 169, groups: part_id, 50
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 0.004083 0.007050
## groupTD -0.025151 0.011797
## groupASD:taskslope_children_ssl_indiv_rts_slope -0.013387 0.009894
## groupTD:taskslope_children_ssl_indiv_rts_slope 0.007346 0.013528
## groupASD:taskslope_children_tsl_indiv_rts_slope -0.004989 0.009988
## groupTD:taskslope_children_tsl_indiv_rts_slope 0.021107 0.013281
## groupASD:taskslope_children_vsl_indiv_rts_slope -0.013174 0.009804
## groupTD:taskslope_children_vsl_indiv_rts_slope 0.036026 0.012890
## df t value
## (Intercept) 160.909885 0.579
## groupTD 160.938397 -2.132
## groupASD:taskslope_children_ssl_indiv_rts_slope 120.186965 -1.353
## groupTD:taskslope_children_ssl_indiv_rts_slope 131.217315 0.543
## groupASD:taskslope_children_tsl_indiv_rts_slope 119.041515 -0.499
## groupTD:taskslope_children_tsl_indiv_rts_slope 124.255408 1.589
## groupASD:taskslope_children_vsl_indiv_rts_slope 118.948847 -1.344
## groupTD:taskslope_children_vsl_indiv_rts_slope 127.833987 2.795
## Pr(>|t|)
## (Intercept) 0.56329
## groupTD 0.03453 *
## groupASD:taskslope_children_ssl_indiv_rts_slope 0.17860
## groupTD:taskslope_children_ssl_indiv_rts_slope 0.58805
## groupASD:taskslope_children_tsl_indiv_rts_slope 0.61836
## groupTD:taskslope_children_tsl_indiv_rts_slope 0.11454
## groupASD:taskslope_children_vsl_indiv_rts_slope 0.18160
## groupTD:taskslope_children_vsl_indiv_rts_slope 0.00599 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) gropTD grpASD:tskslp_chldrn_s___
## groupTD -0.598
## grpASD:tskslp_chldrn_s___ -0.702 0.419
## grpTD:tskslp_chldrn_s___ 0.000 -0.553 0.000
## grpASD:tskslp_chldrn_t___ -0.695 0.415 0.495
## grpTD:tskslp_chldrn_t___ 0.000 -0.563 0.000
## grpASD:tskslp_chldrn_v___ -0.708 0.423 0.505
## grpTD:tskslp_chldrn_v___ 0.000 -0.581 0.000
## grpTD:tskslp_chldrn_s___
## groupTD
## grpASD:tskslp_chldrn_s___
## grpTD:tskslp_chldrn_s___
## grpASD:tskslp_chldrn_t___ 0.000
## grpTD:tskslp_chldrn_t___ 0.490
## grpASD:tskslp_chldrn_v___ 0.000
## grpTD:tskslp_chldrn_v___ 0.507
## grpASD:tskslp_chldrn_t___
## groupTD
## grpASD:tskslp_chldrn_s___
## grpTD:tskslp_chldrn_s___
## grpASD:tskslp_chldrn_t___
## grpTD:tskslp_chldrn_t___ 0.000
## grpASD:tskslp_chldrn_v___ 0.500
## grpTD:tskslp_chldrn_v___ 0.000
## grpTD:tskslp_chldrn_t___
## groupTD
## grpASD:tskslp_chldrn_s___
## grpTD:tskslp_chldrn_s___
## grpASD:tskslp_chldrn_t___
## grpTD:tskslp_chldrn_t___
## grpASD:tskslp_chldrn_v___ 0.000
## grpTD:tskslp_chldrn_v___ 0.515
## grpASD:tskslp_chldrn_v___
## groupTD
## grpASD:tskslp_chldrn_s___
## grpTD:tskslp_chldrn_s___
## grpASD:tskslp_chldrn_t___
## grpTD:tskslp_chldrn_t___
## grpASD:tskslp_chldrn_v___
## grpTD:tskslp_chldrn_v___ 0.000anova(m1_slope, m2_slope)## refitting model(s) with ML (instead of REML)## Data: bucld_all_completed_slope
## Models:
## m2_slope: rt_slope ~ group:task + group + (1 | part_id)
## m1_slope: rt_slope ~ group:task + group + (1 + task | part_id)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## m2_slope 10 -626.33 -595.03 323.16 -646.33
## m1_slope 19 -648.37 -588.90 343.18 -686.37 40.038 9 7.479e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1write.csv(bucld_all_completed_slope, "bucld_all_completed_slope_use_this_poster.csv")Accuracy Anova (Ignore)
library("ez")
ezDesign(bucld_all_completed_acc, part_id, task)
all_complete <-
cast(bucld_all_completed_acc, part_id~modality+domain, value = "task", length)
all_complete_id <-
all_complete[which(all_complete$auditory_ling == 1 & all_complete$auditory_nonling == 1 &
all_complete$visual_ling == 1 & all_complete$visual_nonling == 1), "part_id"]
bucld_acc_anova <-
bucld_all_completed_acc[which(bucld_all_completed_acc$part_id %in% all_complete_id),]
#simplied_subj <- str_extract(bucld_all_completed_acc$part_id, "(?<=c_)\\S+")
#test <- cbind(simplied_subj, bucld_all_completed_acc)
ezDesign(bucld_acc_anova, part_id, task)
# temp = as.data.frame(table(bucld_all_completed_acc$part_id))
# rm_id <- temp[temp$Freq<2,]$Var1
# bucld_all_completed_acc <- bucld_all_completed_acc[-which(bucld_all_completed_acc$part_id %in% rm_id),]
ezANOVA(data=bucld_acc_anova,
dv=.(accuracy),
wid=.(part_id),
between=.(group),
within=.(domain, modality),
between_covariates = age_at_web_year,
type = 3
)
ezANOVA(data=bucld_acc_anova,
dv=.(accuracy),
wid=.(part_id),
between=.(group),
within=.(task),
between_covariates = age_at_web_year,
type = 3
)
all_completed_acc_id <- unique(bucld_acc_anova$part_id)
all_completed_acc_id <- as.data.frame(all_completed_acc_id)
anova_demo <-
merge(bucld_demo_all[,c("part_id", "group", "sex", "age_at_web_year")], all_completed_acc_id,
by.x = "part_id", by.y = "all_completed_acc_id",
all.y = TRUE)Anova demographic (ignore)
Entropy Anova (Ignore)
RT Anova (Ignore)
Slope Anova (Ignore)
Accuracy against chance for TD and ASD
t_test_against_chance <-
function(x){
test <- t.test(x, mu=0.5, alternative= "greater")
data.frame(p_value = test$p.value,
df = test$parameter,
t_stat = test$statistic)
}
#TD without outliers
sapply(colnames(td_sl)[c(6:10, 25)],
function(x) t_test_against_chance(td_sl[,x]))## accuracy_children_lsl_random_2afc_accuracies
## p_value 0.01241873
## df 10
## t_stat 2.637567
## accuracy_children_ssl_accuracies accuracy_children_tsl_accuracies
## p_value 0.0515594 0.0111262
## df 13 15
## t_stat 1.753091 2.548765
## accuracy_children_vsl_accuracies
## p_value 0.01733192
## df 16
## t_stat 2.30846
## accuracy_lsl_predictable_2afc_accuracies
## p_value 0.04269954
## df 3
## t_stat 2.530335
## accuracy_children_lsl_accuracies
## p_value 0.001450041
## df 14
## t_stat 3.599722#ASD without outliers
sapply(colnames(asd_sl)[c(6:10, 25)],
function(x) t_test_against_chance(asd_sl[,x]))## accuracy_children_lsl_random_2afc_accuracies
## p_value 0.9274642
## df 11
## t_stat -1.568463
## accuracy_children_ssl_accuracies accuracy_children_tsl_accuracies
## p_value 0.4364282 0.01961443
## df 28 27
## t_stat 0.1615036 2.166952
## accuracy_children_vsl_accuracies
## p_value 0.03890788
## df 27
## t_stat 1.833235
## accuracy_lsl_predictable_2afc_accuracies
## p_value 0.1491112
## df 14
## t_stat 1.080406
## accuracy_children_lsl_accuracies
## p_value 0.4994865
## df 26
## t_stat 0.001299628TD: marginally significant above chance for SSL accuracy (p = 0.052). Above chance for other tasks. LSL (p < 0.01). TSL, VSL (p <0.05)
ASD: Only VSL (p < 0.05) and TSL (marginal, p = 0.07) above chance.
In the bigger sample, TSL was significantly above chance.
Accuracy Line Plot (no outliers)
library(plyr)## -------------------------------------------------------------------------## You have loaded plyr after dplyr - this is likely to cause problems.
## If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
## library(plyr); library(dplyr)## -------------------------------------------------------------------------##
## Attaching package: 'plyr'## The following objects are masked from 'package:dplyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize## The following objects are masked from 'package:reshape':
##
## rename, round_anylibrary(dplyr)
library(ggplot2)
library(data.table)
library("pastecs")
stat_desc <-
function(df,group,task) {
stat.desc(df[which(df$group == group & df$variable == task),])
}
bucld_sl_bar_all_long <- melt(bucld_sl_bar_all, id.vars=c("part_id", "group"))
bucld_sl_bar_acc <-
bucld_sl_bar_all_long[which(str_detect(
bucld_sl_bar_all_long$variable, "(?<=accuracy_children_)\\S{3}(?=_accuracies)")),]
bucld_sl_bar_acc$variable <-
revalue(bucld_sl_bar_acc$variable, c("accuracy_children_lsl_accuracies"="Letter",
"accuracy_children_vsl_accuracies"="Image",
"accuracy_children_tsl_accuracies"="Tone",
"accuracy_children_ssl_accuracies"="Syllable"))
bucld_sl_bar_acc$variable <-
factor(bucld_sl_bar_acc$variable,levels = c("Image",
"Letter",
"Tone",
"Syllable"))
bucld_sl_bar_acc <-
bucld_sl_bar_acc[order(bucld_sl_bar_acc$variable), ]
bucld_sl_bar_acc$value <-
as.numeric(as.character(bucld_sl_bar_acc$value))
td_tone <- stat_desc(bucld_sl_bar_acc, "TD", "Tone")
td_syllable <- stat_desc(bucld_sl_bar_acc, "TD", "Syllable")
td_image <- stat_desc(bucld_sl_bar_acc, "TD", "Image")
td_letter <- stat_desc(bucld_sl_bar_acc, "TD", "Letter")
asd_tone <- stat_desc(bucld_sl_bar_acc, "ASD", "Tone")
asd_syllable <- stat_desc(bucld_sl_bar_acc, "ASD", "Syllable")
asd_image <- stat_desc(bucld_sl_bar_acc, "ASD", "Image")
asd_letter <- stat_desc(bucld_sl_bar_acc, "ASD", "Letter")
extract_line_graph_stat <- function (x, group, task) {
y <- x[c("nbr.val","mean", "SE.mean", "std.dev"),"value"]
dim(y) <- c(4, 1)
y <- t(y)
colnames(y) <- c("n", "mean", "std_error", "sd")
y <- data.frame(y)
y$group <- group
y$task <- task
return(y)
}
td_tone_stat <- extract_line_graph_stat(td_tone, "TD", "Tone")
td_syllable_stat <- extract_line_graph_stat(td_syllable, "TD", "Syllable")
td_image_stat <- extract_line_graph_stat(td_image, "TD", "Image")
td_letter_stat <- extract_line_graph_stat(td_letter, "TD", "Letter")
asd_tone_stat <- extract_line_graph_stat(asd_tone, "ASD", "Tone")
asd_syllable_stat <- extract_line_graph_stat(asd_syllable, "ASD", "Syllable")
asd_image_stat <- extract_line_graph_stat(asd_image, "ASD", "Image")
asd_letter_stat <- extract_line_graph_stat(asd_letter, "ASD", "Letter")
acc_stat <- rbind(td_tone_stat, td_syllable_stat, td_image_stat, td_letter_stat,
asd_tone_stat, asd_syllable_stat, asd_image_stat, asd_letter_stat)
colnames(acc_stat)[colnames(acc_stat) == "group"] <- "Group"
acc_stat$task <-
factor(acc_stat$task,levels = c("Image",
"Letter",
"Tone",
"Syllable"))
acc_stat <-
acc_stat[order(acc_stat$task), ]
cast(acc_stat, task~Group, value = "mean")## task ASD TD
## 1 Image 0.5535714 0.6085882
## 2 Letter 0.5000370 0.6498667
## 3 Tone 0.5535714 0.5957500
## 4 Syllable 0.5021379 0.5447143cast(acc_stat, task~Group, value = "sd")## task ASD TD
## 1 Image 0.15463013 0.19394782
## 2 Letter 0.14808092 0.16124332
## 3 Tone 0.13081665 0.15026887
## 4 Syllable 0.07128701 0.09543458pd <- position_dodge(width = 0.2)
acc_stat %>%
ggplot(aes(x = task, y = mean, group = Group)) +
geom_line(aes(linetype = Group, color = Group), position = pd, size = 1.8) +
geom_errorbar(aes(ymin = mean - std_error, ymax = mean + std_error), width = .1, position = pd) +
geom_point(aes(color = Group), size = 4, position = pd,show.legend = FALSE) +
geom_point(size = 3, color = "white", position = pd) +
labs(title = "Behavioral Accuracy across Groups",
x = "SL Task", # Change x-axis label
y = "Mean Accuracy") +
theme(plot.title = element_text(hjust = 0.5)) +
theme(
plot.title = element_text(size=16, face="bold"),
axis.title.x = element_text(size=14, face="bold"),
axis.title.y = element_text(size=14, face="bold"),
axis.text=element_text(size=12, face = "bold")
) +
theme(legend.text=element_text(size=14, face="bold"),
legend.title=element_text(size=15, face="bold")) +
theme(
panel.background = element_rect(fill = "white"), # Set plot background to white
legend.key = element_rect(fill = "white"), # Set legend item backgrounds to white
axis.line.x = element_line(colour = "black", size = 1), # Add line to x axis
axis.line.y = element_line(colour = "black", size = 1) # Add line to y axis
) +
geom_hline(yintercept=0.5, color = "grey") +
geom_text(aes(x= 1, y = 0.505, label = paste0("chance", "-level")), size=5) +
geom_text(aes(x= 2.05, y = 0.7, label = paste0("**")), size=7, color="#00BFC4") +
geom_text(aes(x= 3.05, y = 0.7, label = paste0("†")), size=7, color="#00BFC4") +
geom_text(aes(x= 1.05, y = 0.7, label = paste0("*")), size=7, color="#00BFC4") +
geom_text(aes(x= 4.05, y = 0.7, label = paste0("*")), size=7, color="#00BFC4") +
geom_text(aes(x= 0.95, y = 0.52, label = paste0("*")), size=7, color="#F8766d") +
geom_text(aes(x= 2.95, y = 0.52, label = paste0("*")), size=7, color="#F8766d")
# ggsave("behavioral_acc_across_group.png",
# bg="transparent",
# width = 15, height = 15, units = "cm")Scaled Accuracy Line Plot
library(plyr)
library(dplyr)
library(ggplot2)
library(data.table)
library("pastecs")
bucld_sl_bar_acc <-
crossed_random_effect_acc_long[which(str_detect(
crossed_random_effect_acc_long$task, "(?<=accuracy_children_)\\S{3}(?=_accuracies)")),]
bucld_sl_bar_acc$task <-
revalue(bucld_sl_bar_acc$task, c("accuracy_children_lsl_accuracies"="Letter",
"accuracy_children_vsl_accuracies"="Image",
"accuracy_children_tsl_accuracies"="Tone",
"accuracy_children_ssl_accuracies"="Syllable"))
bucld_sl_bar_acc$task <-
factor(bucld_sl_bar_acc$task,levels = c("Image",
"Letter",
"Tone",
"Syllable"))
bucld_sl_bar_acc <-
bucld_sl_bar_acc[order(bucld_sl_bar_acc$task), ]
bucld_sl_bar_acc$value <-
as.numeric(as.character(bucld_sl_bar_acc$value))
stat_desc <-
function(df,group,task) {
stat.desc(df[which(df$group == group & df[,"task"] == task),])
}
td_tone <- stat_desc(bucld_sl_bar_acc, "TD", "Tone")
td_syllable <- stat_desc(bucld_sl_bar_acc, "TD", "Syllable")
td_image <- stat_desc(bucld_sl_bar_acc, "TD", "Image")
td_letter <- stat_desc(bucld_sl_bar_acc, "TD", "Letter")
asd_tone <- stat_desc(bucld_sl_bar_acc, "ASD", "Tone")
asd_syllable <- stat_desc(bucld_sl_bar_acc, "ASD", "Syllable")
asd_image <- stat_desc(bucld_sl_bar_acc, "ASD", "Image")
asd_letter <- stat_desc(bucld_sl_bar_acc, "ASD", "Letter")
extract_line_graph_stat <- function (x, group, task) {
y <- x[c("nbr.val","mean", "SE.mean", "std.dev"),"value"]
dim(y) <- c(4, 1)
y <- t(y)
colnames(y) <- c("n", "mean", "std_error", "sd")
y <- data.frame(y)
y$group <- group
y$task <- task
return(y)
}
td_tone_stat <- extract_line_graph_stat(td_tone, "TD", "Tone")
td_syllable_stat <- extract_line_graph_stat(td_syllable, "TD", "Syllable")
td_image_stat <- extract_line_graph_stat(td_image, "TD", "Image")
td_letter_stat <- extract_line_graph_stat(td_letter, "TD", "Letter")
asd_tone_stat <- extract_line_graph_stat(asd_tone, "ASD", "Tone")
asd_syllable_stat <- extract_line_graph_stat(asd_syllable, "ASD", "Syllable")
asd_image_stat <- extract_line_graph_stat(asd_image, "ASD", "Image")
asd_letter_stat <- extract_line_graph_stat(asd_letter, "ASD", "Letter")
acc_stat <- rbind(td_tone_stat, td_syllable_stat, td_image_stat, td_letter_stat,
asd_tone_stat, asd_syllable_stat, asd_image_stat, asd_letter_stat)
colnames(acc_stat)[colnames(acc_stat) == "group"] <- "Group"
acc_stat$task <-
factor(acc_stat$task,levels = c("Image",
"Letter",
"Tone",
"Syllable"))
acc_stat <-
acc_stat[order(acc_stat$task), ]
cast(acc_stat, task~Group, value = "mean")## task ASD TD
## 1 Image -0.1218838 0.2007498
## 2 Letter -0.3194267 0.5749681
## 3 Tone -0.1111436 0.1945013
## 4 Syllable -0.1704435 0.3530614cast(acc_stat, task~Group, value = "sd")## task ASD TD
## 1 Image 0.9067935 1.1373633
## 2 Letter 0.8839560 0.9625278
## 3 Tone 0.9479562 1.0889157
## 4 Syllable 0.8765217 1.1734322pd <- position_dodge(width = 0.2)
acc_stat %>%
ggplot(aes(x = task, y = mean, group = Group)) +
geom_line(aes(linetype = Group, color = Group), position = pd, size = 1.8) +
geom_errorbar(aes(ymin = mean - std_error, ymax = mean + std_error), width = .1, position = pd) +
geom_point(aes(color = Group), size = 4, position = pd,show.legend = FALSE) +
geom_point(size = 3, color = "white", position = pd) +
labs(title = "Behavioral Accuracy across Groups",
x = "SL Task", # Change x-axis label
y = "Scaled Mean Accuracy (arbitrary unit)") +
theme(plot.title = element_text(hjust = 0.5)) +
theme(
plot.title = element_text(size=16, face="bold"),
axis.title.x = element_text(size=14, face="bold"),
axis.title.y = element_text(size=14, face="bold"),
axis.text=element_text(size=12, face = "bold")
) +
theme(legend.text=element_text(size=14, face="bold"),
legend.title=element_text(size=15, face="bold")) +
theme(
panel.background = element_rect(fill = "white"), # Set plot background to white
legend.key = element_rect(fill = "white"), # Set legend item backgrounds to white
axis.line.x = element_line(colour = "black", size = 1), # Add line to x axis
axis.line.y = element_line(colour = "black", size = 1) # Add line to y axis
) 
# +
# geom_hline(yintercept=0.5, color = "grey") +
# geom_text(aes(x= 1, y = 0.505, label = paste0("chance", "-level")), size=5)
# +
# geom_text(aes(x= 2.05, y = 0.7, label = paste0("**")), size=7, color="#00BFC4") +
# geom_text(aes(x= 3.05, y = 0.7, label = paste0("†")), size=7, color="#00BFC4") +
# geom_text(aes(x= 1.05, y = 0.7, label = paste0("*")), size=7, color="#00BFC4") +
# geom_text(aes(x= 4.05, y = 0.7, label = paste0("*")), size=7, color="#00BFC4") +
# geom_text(aes(x= 0.95, y = 0.52, label = paste0("*")), size=7, color="#F8766d") +
# geom_text(aes(x= 2.95, y = 0.52, label = paste0("*")), size=7, color="#F8766d")
ggsave("behavioral_scaled_acc_across_group.png",
bg="transparent",
width = 15, height = 15, units = "cm")Entropy Line Plot (no outliers)
library(dplyr)
library(ggplot2)
bucld_sl_bar_all_long <- melt(bucld_sl_bar_all, id.vars=c("part_id", "group"))
bucld_sl_bar_entropy <-
bucld_sl_bar_all_long[which(
str_detect(bucld_sl_bar_all_long$variable, "(?<=entropy_children_)\\S{3}(?=_entropy)")),]
bucld_sl_bar_entropy$variable <-
revalue(bucld_sl_bar_entropy$variable, c("entropy_children_lsl_entropy"="Letter",
"entropy_children_vsl_entropy"="Image",
"entropy_children_tsl_entropy"="Tone",
"entropy_children_ssl_entropy"="Syllable"))
bucld_sl_bar_entropy$variable <-
factor(bucld_sl_bar_entropy$variable,levels = c("Image",
"Letter",
"Tone",
"Syllable"))
bucld_sl_bar_entropy <-
bucld_sl_bar_entropy[order(bucld_sl_bar_entropy$variable), ]
bucld_sl_bar_entropy$value <-
as.numeric(as.character(bucld_sl_bar_entropy$value))
library("pastecs")
stat_desc <-
function(df,group,task) {
stat.desc(df[which(df$group == group & df$variable == task),])
}
td_tone <- stat_desc(bucld_sl_bar_entropy, "TD", "Tone")
td_syllable <- stat_desc(bucld_sl_bar_entropy, "TD", "Syllable")
td_image <- stat_desc(bucld_sl_bar_entropy, "TD", "Image")
td_letter <- stat_desc(bucld_sl_bar_entropy, "TD", "Letter")
asd_tone <- stat_desc(bucld_sl_bar_entropy, "ASD", "Tone")
asd_syllable <- stat_desc(bucld_sl_bar_entropy, "ASD", "Syllable")
asd_image <- stat_desc(bucld_sl_bar_entropy, "ASD", "Image")
asd_letter <- stat_desc(bucld_sl_bar_entropy, "ASD", "Letter")
extract_line_graph_stat <- function (x, group, task) {
y <- x[c("nbr.val","mean", "SE.mean"),"value"]
dim(y) <- c(3, 1)
y <- t(y)
colnames(y) <- c("n", "mean", "std_error")
y <- data.frame(y)
y$group <- group
y$task <- task
return(y)
}
td_tone_stat <- extract_line_graph_stat(td_tone, "TD", "Tone")
td_syllable_stat <- extract_line_graph_stat(td_syllable, "TD", "Syllable")
td_image_stat <- extract_line_graph_stat(td_image, "TD", "Image")
td_letter_stat <- extract_line_graph_stat(td_letter, "TD", "Letter")
asd_tone_stat <- extract_line_graph_stat(asd_tone, "ASD", "Tone")
asd_syllable_stat <- extract_line_graph_stat(asd_syllable, "ASD", "Syllable")
asd_image_stat <- extract_line_graph_stat(asd_image, "ASD", "Image")
asd_letter_stat <- extract_line_graph_stat(asd_letter, "ASD", "Letter")
entropy_stat <- rbind(td_tone_stat, td_syllable_stat, td_image_stat, td_letter_stat,
asd_tone_stat, asd_syllable_stat, asd_image_stat, asd_letter_stat)
colnames(entropy_stat)[colnames(entropy_stat) == "group"] <- "Group"
entropy_stat$task <-
factor(entropy_stat$task,levels = c("Image",
"Letter",
"Tone",
"Syllable"))
entropy_stat <-
entropy_stat[order(entropy_stat$task), ]
pd <- position_dodge(width = 0.2)
entropy_stat %>%
ggplot(aes(x = task, y = mean, group = Group)) +
geom_line(aes(linetype = Group, color = Group), position = pd, size = 1.8) +
geom_errorbar(aes(ymin = mean - std_error, ymax = mean + std_error), width = .1, position = pd) +
geom_point(aes(color = Group), size = 4, position = pd,show.legend = FALSE) +
geom_point(size = 3, color = "white", position = pd) +
labs(title = "Behavioral Entropy across Groups",
x = "SL Task", # Change x-axis label
y = "Mean Entropy") +
theme(plot.title = element_text(hjust = 0.5)) +
theme(
plot.title = element_text(size=16, face="bold"),
axis.title.x = element_text(size=14, face="bold"),
axis.title.y = element_text(size=14, face="bold"),
axis.text=element_text(size=12, face = "bold")
) +
theme(legend.text=element_text(size=14, face="bold"),
legend.title=element_text(size=15, face="bold")) +
theme(
panel.background = element_rect(fill = "white"), # Set plot background to white
legend.key = element_rect(fill = "white"), # Set legend item backgrounds to white
axis.line.x = element_line(colour = "black", size = 1), # Add line to x axis
axis.line.y = element_line(colour = "black", size = 1) # Add line to y axis
)
ggsave("behavioral_entropy_across_group.png",
bg="transparent",
width = 15, height = 15, units = "cm")Mean RT Line Plot (no outliers)
library(dplyr)
library(ggplot2)
bucld_sl_bar_all_long <- melt(bucld_sl_bar_all, id.vars=c("part_id", "group"))
bucld_sl_bar_rt <-
bucld_sl_bar_all_long[which(
str_detect(bucld_sl_bar_all_long$variable, "(?<=rt_children_)\\S{3}(?=_indiv_rts)")),]
bucld_sl_bar_rt$variable <-
revalue(bucld_sl_bar_rt$variable, c("rt_children_lsl_indiv_rts"="Letter",
"rt_children_vsl_indiv_rts"="Image",
"rt_children_tsl_indiv_rts"="Tone",
"rt_children_ssl_indiv_rts"="Syllable"))
bucld_sl_bar_rt$variable <-
factor(bucld_sl_bar_rt$variable,levels = c("Image",
"Letter",
"Tone",
"Syllable"))
bucld_sl_bar_rt <-
bucld_sl_bar_rt[order(bucld_sl_bar_rt$variable), ]
bucld_sl_bar_rt$value <-
as.numeric(as.character(bucld_sl_bar_rt$value))
library("pastecs")
stat_desc <-
function(df,group,task) {
stat.desc(df[which(df$group == group & df$variable == task),])
}
td_tone <- stat_desc(bucld_sl_bar_rt, "TD", "Tone")
td_syllable <- stat_desc(bucld_sl_bar_rt, "TD", "Syllable")
td_image <- stat_desc(bucld_sl_bar_rt, "TD", "Image")
td_letter <- stat_desc(bucld_sl_bar_rt, "TD", "Letter")
asd_tone <- stat_desc(bucld_sl_bar_rt, "ASD", "Tone")
asd_syllable <- stat_desc(bucld_sl_bar_rt, "ASD", "Syllable")
asd_image <- stat_desc(bucld_sl_bar_rt, "ASD", "Image")
asd_letter <- stat_desc(bucld_sl_bar_rt, "ASD", "Letter")
extract_line_graph_stat <- function (x, group, task) {
y <- x[c("nbr.val","mean", "SE.mean"),"value"]
dim(y) <- c(3, 1)
y <- t(y)
colnames(y) <- c("n", "mean", "std_error")
y <- data.frame(y)
y$group <- group
y$task <- task
return(y)
}
td_tone_stat <- extract_line_graph_stat(td_tone, "TD", "Tone")
td_syllable_stat <- extract_line_graph_stat(td_syllable, "TD", "Syllable")
td_image_stat <- extract_line_graph_stat(td_image, "TD", "Image")
td_letter_stat <- extract_line_graph_stat(td_letter, "TD", "Letter")
asd_tone_stat <- extract_line_graph_stat(asd_tone, "ASD", "Tone")
asd_syllable_stat <- extract_line_graph_stat(asd_syllable, "ASD", "Syllable")
asd_image_stat <- extract_line_graph_stat(asd_image, "ASD", "Image")
asd_letter_stat <- extract_line_graph_stat(asd_letter, "ASD", "Letter")
rt_stat <- rbind(td_tone_stat, td_syllable_stat, td_image_stat, td_letter_stat,
asd_tone_stat, asd_syllable_stat, asd_image_stat, asd_letter_stat)
colnames(rt_stat)[colnames(rt_stat) == "group"] <- "Group"
rt_stat$task <-
factor(rt_stat$task,levels = c("Image",
"Letter",
"Tone",
"Syllable"))
rt_stat <-
rt_stat[order(rt_stat$task), ]
pd <- position_dodge(width = 0.2)
rt_stat %>%
ggplot(aes(x = task, y = mean, group = Group)) +
geom_line(aes(linetype = Group, color = Group), position = pd, size = 1.8) +
geom_errorbar(aes(ymin = mean - std_error, ymax = mean + std_error), width = .1, position = pd) +
geom_point(aes(color = Group), size = 4, position = pd,show.legend = FALSE) +
geom_point(size = 3, color = "white", position = pd) +
labs(title = "Behavioral Reaction Time across Groups",
x = "SL Task", # Change x-axis label
y = "Mean Reaction Time (ms)") +
theme(plot.title = element_text(hjust = 0.5)) +
theme(
plot.title = element_text(size=16, face="bold"),
axis.title.x = element_text(size=14, face="bold"),
axis.title.y = element_text(size=14, face="bold"),
axis.text=element_text(size=12, face = "bold")
) +
theme(legend.text=element_text(size=14, face="bold"),
legend.title=element_text(size=15, face="bold")) +
theme(
panel.background = element_rect(fill = "white"), # Set plot background to white
legend.key = element_rect(fill = "white"), # Set legend item backgrounds to white
axis.line.x = element_line(colour = "black", size = 1), # Add line to x axis
axis.line.y = element_line(colour = "black", size = 1) # Add line to y axis
)
ggsave("behavioral_rt_across_group.png",
width = 15, height = 15, units = "cm")
write.csv(blast_spoli_data_wide, "blast_spoli_data_all_measures.csv")
write.csv(bucld_all_completed, "blast_spoli_data_all_measures_no_outlier.csv")RT Slope Line Plot (no outliers)
library(dplyr)
library(ggplot2)
bucld_sl_bar_all_long <- melt(bucld_sl_bar_all, id.vars=c("part_id", "group"))
bucld_sl_bar_slope <-
bucld_sl_bar_all_long[which(
str_detect(bucld_sl_bar_all_long$variable, "(?<=slope_children_)\\S{3}(?=_indiv_rts_slope)")),]
bucld_sl_bar_slope$variable <-
revalue(bucld_sl_bar_slope$variable, c("slope_children_lsl_indiv_rts_slope"="Letter",
"slope_children_vsl_indiv_rts_slope"="Image",
"slope_children_tsl_indiv_rts_slope"="Tone",
"slope_children_ssl_indiv_rts_slope"="Syllable"))
bucld_sl_bar_slope$variable <-
factor(bucld_sl_bar_slope$variable,levels = c("Image",
"Letter",
"Tone",
"Syllable"))
bucld_sl_bar_slope <-
bucld_sl_bar_slope[order(bucld_sl_bar_slope$variable), ]
bucld_sl_bar_slope$value <-
as.numeric(as.character(bucld_sl_bar_slope$value))
library("pastecs")
stat_desc <-
function(df,group,task) {
stat.desc(df[which(df$group == group & df$variable == task),])
}
td_tone <- stat_desc(bucld_sl_bar_slope, "TD", "Tone")
td_syllable <- stat_desc(bucld_sl_bar_slope, "TD", "Syllable")
td_image <- stat_desc(bucld_sl_bar_slope, "TD", "Image")
td_letter <- stat_desc(bucld_sl_bar_slope, "TD", "Letter")
asd_tone <- stat_desc(bucld_sl_bar_slope, "ASD", "Tone")
asd_syllable <- stat_desc(bucld_sl_bar_slope, "ASD", "Syllable")
asd_image <- stat_desc(bucld_sl_bar_slope, "ASD", "Image")
asd_letter <- stat_desc(bucld_sl_bar_slope, "ASD", "Letter")
extract_line_graph_stat <- function (x, group, task) {
y <- x[c("nbr.val","mean", "SE.mean"),"value"]
dim(y) <- c(3, 1)
y <- t(y)
colnames(y) <- c("n", "mean", "std_error")
y <- data.frame(y)
y$group <- group
y$task <- task
return(y)
}
td_tone_stat <- extract_line_graph_stat(td_tone, "TD", "Tone")
td_syllable_stat <- extract_line_graph_stat(td_syllable, "TD", "Syllable")
td_image_stat <- extract_line_graph_stat(td_image, "TD", "Image")
td_letter_stat <- extract_line_graph_stat(td_letter, "TD", "Letter")
asd_tone_stat <- extract_line_graph_stat(asd_tone, "ASD", "Tone")
asd_syllable_stat <- extract_line_graph_stat(asd_syllable, "ASD", "Syllable")
asd_image_stat <- extract_line_graph_stat(asd_image, "ASD", "Image")
asd_letter_stat <- extract_line_graph_stat(asd_letter, "ASD", "Letter")
slope_stat <- rbind(td_tone_stat, td_syllable_stat, td_image_stat, td_letter_stat,
asd_tone_stat, asd_syllable_stat, asd_image_stat, asd_letter_stat)
slope_stat$task <-
factor(slope_stat$task,levels = c("Image",
"Letter",
"Tone",
"Syllable"))
slope_stat <-
slope_stat[order(slope_stat$task), ]
pd <- position_dodge(width = 0.2)
colnames(slope_stat)[colnames(slope_stat) == "group"] <- "Group"
pd <- position_dodge(width = 0.2)
slope_stat %>%
ggplot(aes(x = task, y = mean, group = Group)) +
geom_line(aes(linetype = Group, color = Group), position = pd, size = 1.8) +
geom_errorbar(aes(ymin = mean - std_error, ymax = mean + std_error), width = .1, position = pd) +
geom_point(aes(color = Group), size = 4, position = pd,show.legend = FALSE) +
geom_point(size = 3, color = "white", position = pd) +
labs(title = "Behavioral Reaction Time Slope across Groups",
x = "SL Task", # Change x-axis label
y = "Reaction Time Slope (arbitrary unit/trial)") +
theme(plot.title = element_text(hjust = 0.5)) +
theme(
plot.title = element_text(size=16, face="bold"),
axis.title.x = element_text(size=14, face="bold"),
axis.title.y = element_text(size=14, face="bold"),
axis.text=element_text(size=12, face = "bold")
) +
theme(legend.text=element_text(size=14, face="bold"),
legend.title=element_text(size=15, face="bold")) +
theme(
panel.background = element_rect(fill = "white"), # Set plot background to white
legend.key = element_rect(fill = "white"), # Set legend item backgrounds to white
axis.line.x = element_line(colour = "black", size = 1), # Add line to x axis
axis.line.y = element_line(colour = "black", size = 1) # Add line to y axis
) #+
#geom_text(aes(x= 1, y = 0.015, label = paste0("†")), size=7)
ggsave(
"behavioral_slope_across_group.png",
width = 15, height = 15, units = "cm"
)Multiple Regression for Accuracy
library("data.table")
bucld_mulreg <- bucld_all_completed
bucld_mulreg$group_dummy <-
revalue(bucld_all_completed$group, c("TD" = 1,
"ASD" = 0))
ssl_lsl_mulreg <-
lm(accuracy_children_ssl_accuracies~accuracy_children_lsl_accuracies*group,
data=bucld_mulreg)
summary(ssl_lsl_mulreg)##
## Call:
## lm(formula = accuracy_children_ssl_accuracies ~ accuracy_children_lsl_accuracies *
## group, data = bucld_mulreg)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.131748 -0.051073 -0.005971 0.053673 0.126073
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 0.51850 0.04803 10.795
## accuracy_children_lsl_accuracies -0.02507 0.09231 -0.272
## groupTD -0.28697 0.10077 -2.848
## accuracy_children_lsl_accuracies:groupTD 0.53847 0.16229 3.318
## Pr(>|t|)
## (Intercept) 2.31e-12 ***
## accuracy_children_lsl_accuracies 0.78768
## groupTD 0.00752 **
## accuracy_children_lsl_accuracies:groupTD 0.00222 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.06964 on 33 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.3769, Adjusted R-squared: 0.3203
## F-statistic: 6.655 on 3 and 33 DF, p-value: 0.001225#Interaction between group significant
ssl_lsl_mulreg_vsl_out <-
lm(accuracy_children_ssl_accuracies~group*accuracy_children_lsl_accuracies + accuracy_children_vsl_accuracies,
data=bucld_mulreg)
summary(ssl_lsl_mulreg_vsl_out)##
## Call:
## lm(formula = accuracy_children_ssl_accuracies ~ group * accuracy_children_lsl_accuracies +
## accuracy_children_vsl_accuracies, data = bucld_mulreg)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.129799 -0.049510 -0.007401 0.052976 0.116851
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 0.536587 0.056915 9.428
## groupTD -0.300060 0.105483 -2.845
## accuracy_children_lsl_accuracies 0.001609 0.106288 0.015
## accuracy_children_vsl_accuracies -0.054786 0.086916 -0.630
## groupTD:accuracy_children_lsl_accuracies 0.556938 0.169110 3.293
## Pr(>|t|)
## (Intercept) 1.28e-10 ***
## groupTD 0.00781 **
## accuracy_children_lsl_accuracies 0.98802
## accuracy_children_vsl_accuracies 0.53310
## groupTD:accuracy_children_lsl_accuracies 0.00248 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.07138 on 31 degrees of freedom
## (16 observations deleted due to missingness)
## Multiple R-squared: 0.3839, Adjusted R-squared: 0.3044
## F-statistic: 4.829 on 4 and 31 DF, p-value: 0.003818lsl_ssl_mulreg <-
lm(accuracy_children_lsl_accuracies~accuracy_children_ssl_accuracies*group,
data=bucld_mulreg)
summary(lsl_ssl_mulreg)##
## Call:
## lm(formula = accuracy_children_lsl_accuracies ~ accuracy_children_ssl_accuracies *
## group, data = bucld_mulreg)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.28049 -0.08005 0.00051 0.05408 0.46617
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 0.5533 0.1959 2.824
## accuracy_children_ssl_accuracies -0.1075 0.3834 -0.280
## groupTD -0.6986 0.3196 -2.185
## accuracy_children_ssl_accuracies:groupTD 1.5120 0.5856 2.582
## Pr(>|t|)
## (Intercept) 0.00799 **
## accuracy_children_ssl_accuracies 0.78085
## groupTD 0.03606 *
## accuracy_children_ssl_accuracies:groupTD 0.01446 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1396 on 33 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.3602, Adjusted R-squared: 0.302
## F-statistic: 6.193 on 3 and 33 DF, p-value: 0.001859#Interaction significant
# tsl_ssl_mulreg <-
# lm(accuracy_children_tsl_accuracies~accuracy_children_ssl_accuracies*group,
# data=bucld_mulreg)
# summary(tsl_ssl_mulreg)
#
# ssl_tsl_mulreg <-
# lm(accuracy_children_ssl_accuracies~accuracy_children_tsl_accuracies*group,
# data=bucld_mulreg)
# summary(ssl_tsl_mulreg)
vsl_lsl_mulreg <-
lm(accuracy_children_vsl_accuracies~accuracy_children_lsl_accuracies*group,
data=bucld_mulreg)
summary(vsl_lsl_mulreg)##
## Call:
## lm(formula = accuracy_children_vsl_accuracies ~ accuracy_children_lsl_accuracies *
## group, data = bucld_mulreg)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.27214 -0.07365 -0.00557 0.06375 0.38005
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 0.3027 0.1032 2.934
## accuracy_children_lsl_accuracies 0.5217 0.1964 2.657
## groupTD -0.1258 0.1983 -0.634
## accuracy_children_lsl_accuracies:groupTD 0.1356 0.3205 0.423
## Pr(>|t|)
## (Intercept) 0.00588 **
## accuracy_children_lsl_accuracies 0.01180 *
## groupTD 0.52997
## accuracy_children_lsl_accuracies:groupTD 0.67479
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1461 on 35 degrees of freedom
## (13 observations deleted due to missingness)
## Multiple R-squared: 0.2909, Adjusted R-squared: 0.2301
## F-statistic: 4.785 on 3 and 35 DF, p-value: 0.006744# LSL significant
lsl_vsl_mulreg <-
lm(accuracy_children_lsl_accuracies~accuracy_children_vsl_accuracies*group,
data=bucld_mulreg)
summary(lsl_vsl_mulreg)##
## Call:
## lm(formula = accuracy_children_lsl_accuracies ~ accuracy_children_vsl_accuracies *
## group, data = bucld_mulreg)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.26247 -0.06802 -0.01883 0.04350 0.33950
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 0.22487 0.10353 2.172
## accuracy_children_vsl_accuracies 0.49458 0.17680 2.797
## groupTD 0.13573 0.16326 0.831
## accuracy_children_vsl_accuracies:groupTD -0.01681 0.26676 -0.063
## Pr(>|t|)
## (Intercept) 0.03671 *
## accuracy_children_vsl_accuracies 0.00832 **
## groupTD 0.41140
## accuracy_children_vsl_accuracies:groupTD 0.95011
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1351 on 35 degrees of freedom
## (13 observations deleted due to missingness)
## Multiple R-squared: 0.4007, Adjusted R-squared: 0.3493
## F-statistic: 7.799 on 3 and 35 DF, p-value: 0.0004077# VSL significant
ssl_vsl_mulreg <-
lm(accuracy_children_ssl_accuracies~accuracy_children_vsl_accuracies*group,
data=bucld_mulreg)
summary(ssl_vsl_mulreg)##
## Call:
## lm(formula = accuracy_children_ssl_accuracies ~ accuracy_children_vsl_accuracies *
## group, data = bucld_mulreg)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.12699 -0.05179 -0.00949 0.06245 0.11680
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 0.56052 0.05274 10.628
## accuracy_children_vsl_accuracies -0.09854 0.09147 -1.077
## groupTD -0.21714 0.08823 -2.461
## accuracy_children_vsl_accuracies:groupTD 0.43255 0.14344 3.015
## Pr(>|t|)
## (Intercept) 1.19e-12 ***
## accuracy_children_vsl_accuracies 0.28852
## groupTD 0.01877 *
## accuracy_children_vsl_accuracies:groupTD 0.00468 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.07333 on 36 degrees of freedom
## (12 observations deleted due to missingness)
## Multiple R-squared: 0.2687, Adjusted R-squared: 0.2078
## F-statistic: 4.41 on 3 and 36 DF, p-value: 0.00967# Interaction significant
ssl_vsl_mulreg_lsl_out <-
lm(accuracy_children_ssl_accuracies~accuracy_children_vsl_accuracies*group + accuracy_children_lsl_accuracies,
data=bucld_mulreg)
summary(ssl_vsl_mulreg_lsl_out)##
## Call:
## lm(formula = accuracy_children_ssl_accuracies ~ accuracy_children_vsl_accuracies *
## group + accuracy_children_lsl_accuracies, data = bucld_mulreg)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.137972 -0.048776 0.000646 0.058321 0.126709
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 0.53657 0.06096 8.803
## accuracy_children_vsl_accuracies -0.19036 0.10871 -1.751
## groupTD -0.21910 0.09341 -2.345
## accuracy_children_lsl_accuracies 0.15469 0.09830 1.574
## accuracy_children_vsl_accuracies:groupTD 0.42392 0.14825 2.860
## Pr(>|t|)
## (Intercept) 6.14e-10 ***
## accuracy_children_vsl_accuracies 0.08982 .
## groupTD 0.02558 *
## accuracy_children_lsl_accuracies 0.12570
## accuracy_children_vsl_accuracies:groupTD 0.00752 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.07377 on 31 degrees of freedom
## (16 observations deleted due to missingness)
## Multiple R-squared: 0.3419, Adjusted R-squared: 0.257
## F-statistic: 4.026 on 4 and 31 DF, p-value: 0.009611vsl_ssl_mulreg <-
lm(accuracy_children_vsl_accuracies~accuracy_children_ssl_accuracies*group,
data=bucld_mulreg)
summary(vsl_ssl_mulreg)##
## Call:
## lm(formula = accuracy_children_vsl_accuracies ~ accuracy_children_ssl_accuracies *
## group, data = bucld_mulreg)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.24866 -0.07892 -0.00494 0.05731 0.47128
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 0.7969 0.2175 3.664
## accuracy_children_ssl_accuracies -0.4772 0.4259 -1.120
## groupTD -0.8775 0.3337 -2.629
## accuracy_children_ssl_accuracies:groupTD 1.7426 0.6233 2.796
## Pr(>|t|)
## (Intercept) 0.000793 ***
## accuracy_children_ssl_accuracies 0.269960
## groupTD 0.012507 *
## accuracy_children_ssl_accuracies:groupTD 0.008255 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1552 on 36 degrees of freedom
## (12 observations deleted due to missingness)
## Multiple R-squared: 0.2207, Adjusted R-squared: 0.1557
## F-statistic: 3.398 on 3 and 36 DF, p-value: 0.02804# Interaction Significant
# lsl_tsl_mulreg <-
# lm(accuracy_children_lsl_accuracies~accuracy_children_tsl_accuracies*group,
# data=bucld_mulreg)
# summary(lsl_tsl_mulreg)
# tsl_lsl_mulreg <-
# lm(accuracy_children_tsl_accuracies~accuracy_children_lsl_accuracies*group,
# data=bucld_mulreg)
# summary(tsl_lsl_mulreg)
vsl_tsl_mulreg <-
lm(accuracy_children_vsl_accuracies~accuracy_children_tsl_accuracies*group,
data=bucld_mulreg)
summary(vsl_tsl_mulreg)##
## Call:
## lm(formula = accuracy_children_vsl_accuracies ~ accuracy_children_tsl_accuracies *
## group, data = bucld_mulreg)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.33206 -0.08680 -0.03866 0.05630 0.40354
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 0.36508 0.15731 2.321
## accuracy_children_tsl_accuracies 0.34717 0.27006 1.285
## groupTD 0.06720 0.24198 0.278
## accuracy_children_tsl_accuracies:groupTD -0.08079 0.40201 -0.201
## Pr(>|t|)
## (Intercept) 0.0262 *
## accuracy_children_tsl_accuracies 0.2071
## groupTD 0.7829
## accuracy_children_tsl_accuracies:groupTD 0.8419
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.17 on 35 degrees of freedom
## (13 observations deleted due to missingness)
## Multiple R-squared: 0.07201, Adjusted R-squared: -0.007529
## F-statistic: 0.9053 on 3 and 35 DF, p-value: 0.4484tsl_vsl_mulreg <-
lm(accuracy_children_tsl_accuracies~accuracy_children_vsl_accuracies*group,
data=bucld_mulreg)
summary(tsl_vsl_mulreg)##
## Call:
## lm(formula = accuracy_children_tsl_accuracies ~ accuracy_children_vsl_accuracies *
## group, data = bucld_mulreg)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.21748 -0.08715 -0.01625 0.06388 0.38718
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 0.43958 0.10467 4.200
## accuracy_children_vsl_accuracies 0.22958 0.17940 1.280
## groupTD 0.05241 0.16218 0.323
## accuracy_children_vsl_accuracies:groupTD -0.04992 0.26851 -0.186
## Pr(>|t|)
## (Intercept) 0.000174 ***
## accuracy_children_vsl_accuracies 0.209065
## groupTD 0.748478
## accuracy_children_vsl_accuracies:groupTD 0.853582
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1389 on 35 degrees of freedom
## (13 observations deleted due to missingness)
## Multiple R-squared: 0.07538, Adjusted R-squared: -0.003877
## F-statistic: 0.9511 on 3 and 35 DF, p-value: 0.4266Multiple Regression Mean RT
# tsl_vsl_mulreg_rt <-
# lm(rt_children_tsl_indiv_rts~rt_children_vsl_indiv_rts*group,
# data=bucld_mulreg)
# summary(tsl_vsl_mulreg_rt)
#
# vsl_tsl_mulreg_rt <-
# lm(rt_children_vsl_indiv_rts~rt_children_tsl_indiv_rts*group,
# data=bucld_mulreg)
# summary(vsl_tsl_mulreg_rt)
# lsl_vsl_mulreg_rt <-
# lm(rt_children_lsl_indiv_rts~rt_children_vsl_indiv_rts*group,
# data=bucld_mulreg)
# summary(lsl_vsl_mulreg_rt)
#
#
# vsl_lsl_mulreg_rt <-
# lm(rt_children_vsl_indiv_rts~rt_children_lsl_indiv_rts*group,
# data=bucld_mulreg)
# summary(vsl_lsl_mulreg_rt)
# ssl_lsl_mulreg_rt <-
# lm(rt_children_ssl_indiv_rts~rt_children_lsl_indiv_rts*group,
# data=bucld_mulreg)
# summary(ssl_lsl_mulreg_rt)
# lsl_ssl_mulreg_rt <-
# lm(rt_children_lsl_indiv_rts~rt_children_ssl_indiv_rts*group,
# data=bucld_mulreg)
# summary(lsl_ssl_mulreg_rt)
ssl_tsl_mulreg_rt <-
lm(rt_children_ssl_indiv_rts~rt_children_tsl_indiv_rts*group,
data=bucld_mulreg)
summary(ssl_tsl_mulreg_rt)##
## Call:
## lm(formula = rt_children_ssl_indiv_rts ~ rt_children_tsl_indiv_rts *
## group, data = bucld_mulreg)
##
## Residuals:
## Min 1Q Median 3Q Max
## -430.99 -60.48 21.91 95.05 287.97
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 431.47465 45.74034 9.433 1.75e-10
## rt_children_tsl_indiv_rts 0.03803 0.23325 0.163 0.8716
## groupTD 161.01627 89.80082 1.793 0.0831
## rt_children_tsl_indiv_rts:groupTD -0.50853 0.33255 -1.529 0.1367
##
## (Intercept) ***
## rt_children_tsl_indiv_rts
## groupTD .
## rt_children_tsl_indiv_rts:groupTD
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 156.2 on 30 degrees of freedom
## (18 observations deleted due to missingness)
## Multiple R-squared: 0.1273, Adjusted R-squared: 0.04008
## F-statistic: 1.459 on 3 and 30 DF, p-value: 0.2454tsl_ssl_mulreg_rt <-
lm(rt_children_tsl_indiv_rts~rt_children_ssl_indiv_rts*group,
data=bucld_mulreg)
summary(tsl_ssl_mulreg_rt)##
## Call:
## lm(formula = rt_children_tsl_indiv_rts ~ rt_children_ssl_indiv_rts *
## group, data = bucld_mulreg)
##
## Residuals:
## Min 1Q Median 3Q Max
## -381.10 -63.86 23.66 90.13 241.16
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 128.92463 86.84554 1.485 0.14810
## rt_children_ssl_indiv_rts 0.02675 0.18627 0.144 0.88678
## groupTD 629.32769 189.29328 3.325 0.00234 **
## rt_children_ssl_indiv_rts:groupTD -1.09628 0.38795 -2.826 0.00831 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 148.7 on 30 degrees of freedom
## (18 observations deleted due to missingness)
## Multiple R-squared: 0.3146, Adjusted R-squared: 0.246
## F-statistic: 4.589 on 3 and 30 DF, p-value: 0.00926# ssl_vsl_mulreg_rt <-
# lm(rt_children_ssl_indiv_rts~rt_children_vsl_indiv_rts*group,
# data=bucld_mulreg)
# summary(ssl_vsl_mulreg_rt)
#
# vsl_ssl_mulreg_rt <-
# lm(rt_children_vsl_indiv_rts~rt_children_ssl_indiv_rts*group,
# data=bucld_mulreg)
# summary(vsl_ssl_mulreg_rt)
# lsl_tsl_mulreg_rt <-
# lm(rt_children_lsl_indiv_rts~rt_children_tsl_indiv_rts*group,
# data=bucld_mulreg)
# summary(lsl_tsl_mulreg_rt)
#
# tsl_lsl_mulreg_rt <-
# lm(rt_children_tsl_indiv_rts~rt_children_lsl_indiv_rts*group,
# data=bucld_mulreg)
# summary(tsl_lsl_mulreg_rt)Multiple regression rt slope (no interaction siginificant)
Plot Accuracy Correlation of SL taks across groups
library(ggpubr)## Loading required package: magrittr##
## Attaching package: 'magrittr'## The following object is masked from 'package:pastecs':
##
## extract##
## Attaching package: 'ggpubr'## The following object is masked from 'package:plyr':
##
## mutatecolnames(bucld_mulreg)[colnames(bucld_mulreg) == "group"] <- "Group"
bucld_mulreg[,c(1:46)] %>%
ggplot(aes(x = accuracy_children_ssl_accuracies, y = accuracy_children_lsl_accuracies, color = Group)) +
geom_point(size = 4, alpha = 0.8) +
scale_fill_brewer(palette = "Set2") +
labs(title = "Syllable and Letter SL Accuracy Correlation",
y = "Letter SL Accuracy", # Change x-axis label
x = "Syllable SL Accuracy") +
theme(plot.title = element_text(hjust = 0.5)) +
theme(
plot.title = element_text(size=16, face="bold"),
axis.title.x = element_text(size=14, face="bold"),
axis.title.y = element_text(size=14, face="bold"),
axis.text=element_text(size=12, face = "bold")
) +
theme(legend.text=element_text(size=14, face="bold")) +
theme(
panel.background = element_rect(fill = "white"),
legend.title = element_text(size = 16, face = "bold"),# Set plot background to white
legend.key = element_rect(fill = "white"), # Set legend item backgrounds to white
axis.line.x = element_line(colour = "black", size = 1), # Add line to x axis
axis.line.y = element_line(colour = "black", size = 1) # Add line to y axis
) +
geom_smooth(data = subset(bucld_mulreg[,c(1:46)], Group == "TD"),
aes(x = accuracy_children_ssl_accuracies, y = accuracy_children_lsl_accuracies),
method=lm, se=FALSE, show.legend = F, inherit.aes = F, color = "#00BFC4") +
geom_smooth(data = subset(bucld_mulreg[,c(1:46)], Group == "ASD"),
aes(x = accuracy_children_ssl_accuracies, y = accuracy_children_lsl_accuracies),
method=lm, se=FALSE, show.legend = F, inherit.aes = F, color = "#F8766d") +
stat_cor(data = subset(bucld_mulreg[,c(1:46)], Group == "TD"),
aes(x = accuracy_children_ssl_accuracies, y = accuracy_children_lsl_accuracies),
method = "pearson", label.x = 0.55, label.y = 0.92,
inherit.aes = F, color = "#00BFC4", size = 5) +
stat_cor(data = subset(bucld_mulreg[,c(1:46)], Group == "ASD"),
aes(x = accuracy_children_ssl_accuracies, y = accuracy_children_lsl_accuracies),
method = "pearson", label.x = 0.6, label.y = 0.49,
inherit.aes = F, color = "#F8766d", size = 5)## Warning: Removed 10 rows containing non-finite values (stat_smooth).## Warning: Removed 5 rows containing non-finite values (stat_smooth).## Warning: Removed 10 rows containing non-finite values (stat_cor).## Warning: Removed 5 rows containing non-finite values (stat_cor).## Warning: Removed 15 rows containing missing values (geom_point).
#+
#geom_text(aes(x = 20, y = 120, label = paste("rho =", "-0.76**")),color = "red",size=4)
ggsave("bucld_ssl_lsl_corr.png",
width = 15, height = 15, units = "cm")## Warning: Removed 10 rows containing non-finite values (stat_smooth).## Warning: Removed 5 rows containing non-finite values (stat_smooth).## Warning: Removed 10 rows containing non-finite values (stat_cor).## Warning: Removed 5 rows containing non-finite values (stat_cor).## Warning: Removed 15 rows containing missing values (geom_point).library(ggpubr)
colnames(bucld_mulreg)[colnames(bucld_mulreg) == "group"] <- "Group"
bucld_mulreg[,c(1:46)] %>%
ggplot(aes(x = accuracy_children_ssl_accuracies, y = accuracy_children_vsl_accuracies, color = Group)) +
geom_point(size = 4, alpha = 0.8) +
scale_fill_brewer(palette = "Set2") +
labs(title = "Syllable and Image SL Correlation",
y = "Image SL Accuracy", # Change x-axis label
x = "Syllable SL Accuracy") +
theme(plot.title = element_text(hjust = 0.5)) +
theme(
plot.title = element_text(size=16, face="bold"),
axis.title.x = element_text(size=14, face="bold"),
axis.title.y = element_text(size=14, face="bold"),
axis.text=element_text(size=12, face = "bold")
) +
theme(legend.text=element_text(size=14, face="bold")) +
theme(
panel.background = element_rect(fill = "white"),
legend.title = element_text(size = 16, face = "bold"),# Set plot background to white
legend.key = element_rect(fill = "white"), # Set legend item backgrounds to white
axis.line.x = element_line(colour = "black", size = 1), # Add line to x axis
axis.line.y = element_line(colour = "black", size = 1) # Add line to y axis
) +
geom_smooth(data = subset(bucld_mulreg[,c(1:46)], Group == "TD"),
aes(x = accuracy_children_ssl_accuracies, y = accuracy_children_vsl_accuracies),
method=lm, se=FALSE, show.legend = F, inherit.aes = F, color = "#00BFC4") +
geom_smooth(data = subset(bucld_mulreg[,c(1:46)], Group == "ASD"),
aes(x = accuracy_children_ssl_accuracies, y = accuracy_children_vsl_accuracies),
method=lm, se=FALSE, show.legend = F, inherit.aes = F, color = "#F8766d") +
stat_cor(data = subset(bucld_mulreg[,c(1:46)], Group == "TD"),
aes(x = accuracy_children_ssl_accuracies, y = accuracy_children_vsl_accuracies),
method = "pearson", label.x = 0.55, label.y = 0.92,
inherit.aes = F, color = "#00BFC4", size = 5) +
stat_cor(data = subset(bucld_mulreg[,c(1:46)], Group == "ASD"),
aes(x = accuracy_children_ssl_accuracies, y = accuracy_children_vsl_accuracies),
method = "pearson", label.x = 0.6, label.y = 0.49,
inherit.aes = F, color = "#F8766d", size = 5)## Warning: Removed 8 rows containing non-finite values (stat_smooth).## Warning: Removed 4 rows containing non-finite values (stat_smooth).## Warning: Removed 8 rows containing non-finite values (stat_cor).## Warning: Removed 4 rows containing non-finite values (stat_cor).## Warning: Removed 12 rows containing missing values (geom_point).
#+
#geom_text(aes(x = 20, y = 120, label = paste("rho =", "-0.76**")),color = "red",size=4)
ggsave("bucld_ssl_vsl_corr.png",
width = 15, height = 15, units = "cm")## Warning: Removed 8 rows containing non-finite values (stat_smooth).## Warning: Removed 4 rows containing non-finite values (stat_smooth).## Warning: Removed 8 rows containing non-finite values (stat_cor).## Warning: Removed 4 rows containing non-finite values (stat_cor).## Warning: Removed 12 rows containing missing values (geom_point).library("ggpubr")
bucld_mulreg[,c(1:46)] %>%
ggplot(aes(x = accuracy_children_lsl_accuracies, y = accuracy_children_vsl_accuracies, color = Group)) +
geom_point(size = 4, alpha = 0.8) +
scale_fill_brewer(palette = "Set2") +
labs(title = "Letter and Image SL Correlation",
x = "Letter SL Accuracy",
y = "Image SL Accuracy") +
theme(plot.title = element_text(hjust = 0.5)) +
theme(
plot.title = element_text(size=16, face="bold"),
axis.title.x = element_text(size=14, face="bold"),
axis.title.y = element_text(size=14, face="bold"),
axis.text=element_text(size=12, face = "bold")
) +
theme(legend.text=element_text(size=14, face="bold")) +
theme(
panel.background = element_rect(fill = "white"),
legend.title = element_text(size = 16, face = "bold"),# Set plot background to white
legend.key = element_rect(fill = "white"), # Set legend item backgrounds to white
axis.line.x = element_line(colour = "black", size = 1), # Add line to x axis
axis.line.y = element_line(colour = "black", size = 1) # Add line to y axis
) +
geom_smooth(data = subset(bucld_mulreg[,c(1:46)], Group == "TD"),
aes(x = accuracy_children_lsl_accuracies, y = accuracy_children_vsl_accuracies),
method=lm, se=FALSE, show.legend = F, inherit.aes = F, color = "#00BFC4") +
geom_smooth(data = subset(bucld_mulreg[,c(1:46)], Group == "ASD"),
aes(x = accuracy_children_lsl_accuracies, y = accuracy_children_vsl_accuracies),
method=lm, se=FALSE, show.legend = F, inherit.aes = F, color = "#F8766d") +
stat_cor(data = subset(bucld_mulreg[,c(1:46)], Group == "TD"),
aes(x = accuracy_children_lsl_accuracies, y = accuracy_children_vsl_accuracies),
method = "pearson", label.x = 0.55, label.y = 0.92,
inherit.aes = F, color = "#00BFC4", size = 5) +
stat_cor(data = subset(bucld_mulreg[,c(1:46)], Group == "ASD"),
aes(x = accuracy_children_lsl_accuracies, y = accuracy_children_vsl_accuracies),
method = "pearson", label.x = 0.6, label.y = 0.49,
inherit.aes = F, color = "#F8766d", size = 5)## Warning: Removed 8 rows containing non-finite values (stat_smooth).## Warning: Removed 5 rows containing non-finite values (stat_smooth).## Warning: Removed 8 rows containing non-finite values (stat_cor).## Warning: Removed 5 rows containing non-finite values (stat_cor).## Warning: Removed 13 rows containing missing values (geom_point).
#+
#geom_text(aes(x = 20, y = 120, label = paste("rho =", "-0.76**")),color = "red",size=4)
ggsave("bucld_ssl_vsl_corr.png",
width = 15, height = 15, units = "cm")## Warning: Removed 8 rows containing non-finite values (stat_smooth).## Warning: Removed 5 rows containing non-finite values (stat_smooth).## Warning: Removed 8 rows containing non-finite values (stat_cor).## Warning: Removed 5 rows containing non-finite values (stat_cor).## Warning: Removed 13 rows containing missing values (geom_point).RT slope and vsl
library("ggpubr")
bucld_mulreg[,c(1:46)] %>%
ggplot(aes(x = accuracy_children_vsl_accuracies, y = slope_children_vsl_indiv_rts_slope, color = Group)) +
geom_point(size = 4, alpha = 0.8) +
scale_fill_brewer(palette = "Set2") +
labs(title = "Image Accuracy and Reaction Time Slope Correlation",
x = "Image SL Accuracy",
y = "Image SL Reaction Time Slope (arbitrary unit/ trial") +
theme(plot.title = element_text(hjust = 0.5)) +
theme(
plot.title = element_text(size=16, face="bold"),
axis.title.x = element_text(size=14, face="bold"),
axis.title.y = element_text(size=14, face="bold"),
axis.text=element_text(size=12, face = "bold")
) +
theme(legend.text=element_text(size=14, face="bold")) +
theme(
panel.background = element_rect(fill = "white"),
legend.title = element_text(size = 16, face = "bold"),# Set plot background to white
legend.key = element_rect(fill = "white"), # Set legend item backgrounds to white
axis.line.x = element_line(colour = "black", size = 1), # Add line to x axis
axis.line.y = element_line(colour = "black", size = 1) # Add line to y axis
) +
geom_smooth(data = subset(bucld_mulreg[,c(1:46)], Group == "TD"),
aes(x = accuracy_children_vsl_accuracies, y = slope_children_vsl_indiv_rts_slope),
method=lm, se=FALSE, show.legend = F, inherit.aes = F, color = "#00BFC4") +
geom_smooth(data = subset(bucld_mulreg[,c(1:46)], Group == "ASD"),
aes(x = accuracy_children_vsl_accuracies, y = slope_children_vsl_indiv_rts_slope),
method=lm, se=FALSE, show.legend = F, inherit.aes = F, color = "#F8766d") +
stat_cor(data = subset(bucld_mulreg[,c(1:46)], Group == "TD"),
aes(x = accuracy_children_vsl_accuracies, y = slope_children_vsl_indiv_rts_slope),
method = "pearson", label.x = 0.7, label.y = 0.05,
inherit.aes = F, color = "#00BFC4", size = 5) +
stat_cor(data = subset(bucld_mulreg[,c(1:46)], Group == "ASD"),
aes(x = accuracy_children_vsl_accuracies, y = slope_children_vsl_indiv_rts_slope),
method = "pearson", label.x = 0.7, label.y = 0.04,
inherit.aes = F, color = "#F8766d", size = 5)## Warning: Removed 4 rows containing non-finite values (stat_smooth).## Warning: Removed 3 rows containing non-finite values (stat_smooth).## Warning: Removed 4 rows containing non-finite values (stat_cor).## Warning: Removed 3 rows containing non-finite values (stat_cor).## Warning: Removed 7 rows containing missing values (geom_point).
#+
#geom_text(aes(x = 20, y = 120, label = paste("rho =", "-0.76**")),color = "red",size=4)
ggsave("bucld_vsl_acc_slope_corr.png",
width = 15, height = 15, units = "cm")## Warning: Removed 4 rows containing non-finite values (stat_smooth).## Warning: Removed 3 rows containing non-finite values (stat_smooth).## Warning: Removed 4 rows containing non-finite values (stat_cor).## Warning: Removed 3 rows containing non-finite values (stat_cor).## Warning: Removed 7 rows containing missing values (geom_point).library("ggpubr")
bucld_mulreg[,c(1:46)] %>%
ggplot(aes(x = accuracy_children_lsl_accuracies, y = slope_children_lsl_indiv_rts_slope, color = Group)) +
geom_point(size = 4, alpha = 0.8) +
scale_fill_brewer(palette = "Set2") +
labs(title = "Letter Accuracy and Reaction Time Slope Correlation",
x = "Letter SL Accuracy",
y = "Letter SL Reaction Time Slope (arbitrary unit/ trial") +
theme(plot.title = element_text(hjust = 0.5)) +
theme(
plot.title = element_text(size=16, face="bold"),
axis.title.x = element_text(size=14, face="bold"),
axis.title.y = element_text(size=14, face="bold"),
axis.text=element_text(size=12, face = "bold")
) +
theme(legend.text=element_text(size=14, face="bold")) +
theme(
panel.background = element_rect(fill = "white"),
legend.title = element_text(size = 16, face = "bold"),# Set plot background to white
legend.key = element_rect(fill = "white"), # Set legend item backgrounds to white
axis.line.x = element_line(colour = "black", size = 1), # Add line to x axis
axis.line.y = element_line(colour = "black", size = 1) # Add line to y axis
) +
geom_smooth(data = subset(bucld_mulreg[,c(1:46)], Group == "TD"),
aes(x = accuracy_children_lsl_accuracies, y = slope_children_lsl_indiv_rts_slope),
method=lm, se=FALSE, show.legend = F, inherit.aes = F, color = "#00BFC4") +
geom_smooth(data = subset(bucld_mulreg[,c(1:46)], Group == "ASD"),
aes(x = accuracy_children_lsl_accuracies, y = slope_children_lsl_indiv_rts_slope),
method=lm, se=FALSE, show.legend = F, inherit.aes = F, color = "#F8766d") +
stat_cor(data = subset(bucld_mulreg[,c(1:46)], Group == "TD"),
aes(x = accuracy_children_lsl_accuracies, y = slope_children_lsl_indiv_rts_slope),
method = "pearson", label.x = 0.7, label.y = 0.05,
inherit.aes = F, color = "#00BFC4", size = 5) +
stat_cor(data = subset(bucld_mulreg[,c(1:46)], Group == "ASD"),
aes(x = accuracy_children_lsl_accuracies, y = slope_children_lsl_indiv_rts_slope),
method = "pearson", label.x = 0.7, label.y = 0.04,
inherit.aes = F, color = "#F8766d", size = 5)## Warning: Removed 6 rows containing non-finite values (stat_smooth).## Warning: Removed 4 rows containing non-finite values (stat_smooth).## Warning: Removed 6 rows containing non-finite values (stat_cor).## Warning: Removed 4 rows containing non-finite values (stat_cor).## Warning: Removed 10 rows containing missing values (geom_point).
#+
#geom_text(aes(x = 20, y = 120, label = paste("rho =", "-0.76**")),color = "red",size=4)
ggsave("bucld_lsl_acc_slope_corr.png",
width = 15, height = 15, units = "cm")## Warning: Removed 6 rows containing non-finite values (stat_smooth).## Warning: Removed 4 rows containing non-finite values (stat_smooth).## Warning: Removed 6 rows containing non-finite values (stat_cor).## Warning: Removed 4 rows containing non-finite values (stat_cor).## Warning: Removed 10 rows containing missing values (geom_point).library("ggpubr")
bucld_mulreg[,c(1:46)] %>%
ggplot(aes(x = rt_children_ssl_indiv_rts, y = rt_children_tsl_indiv_rts, color = Group)) +
geom_point(size = 4, alpha = 0.8) +
scale_fill_brewer(palette = "Set2") +
labs(title = "Syllable and Tone SL RT Correlation",
y = "Tone SL Mean Reaction Time (ms)", # Change x-axis label
x = "Syllable SL Mean Reaction Time (ms)") +
theme(plot.title = element_text(hjust = 0.5)) +
theme(
plot.title = element_text(size=16, face="bold"),
axis.title.x = element_text(size=14, face="bold"),
axis.title.y = element_text(size=14, face="bold"),
axis.text=element_text(size=12, face = "bold")
) +
theme(legend.text=element_text(size=14, face="bold")) +
theme(
panel.background = element_rect(fill = "white"),
legend.title = element_text(size = 16, face = "bold"),# Set plot background to white
legend.key = element_rect(fill = "white"), # Set legend item backgrounds to white
axis.line.x = element_line(colour = "black", size = 1), # Add line to x axis
axis.line.y = element_line(colour = "black", size = 1) # Add line to y axis
) +
geom_smooth(data = subset(bucld_mulreg[,c(1:46)], Group == "TD"),
aes(x = rt_children_ssl_indiv_rts, y = rt_children_tsl_indiv_rts),
method=lm, se=FALSE, show.legend = F, inherit.aes = F, color = "#00BFC4") +
geom_smooth(data = subset(bucld_mulreg[,c(1:46)], Group == "ASD"),
aes(x = rt_children_ssl_indiv_rts, y = rt_children_tsl_indiv_rts),
method=lm, se=FALSE, show.legend = F, inherit.aes = F, color = "#F8766d") +
stat_cor(data = subset(bucld_mulreg[,c(1:46)], Group == "TD"),
aes(x = rt_children_ssl_indiv_rts, y = rt_children_tsl_indiv_rts),
method = "pearson", label.x = -100, label.y = 500,
inherit.aes = F, color = "#00BFC4", size = 5) +
stat_cor(data = subset(bucld_mulreg[,c(1:46)], Group == "ASD"),
aes(x = rt_children_ssl_indiv_rts, y = rt_children_tsl_indiv_rts),
method = "pearson", label.x = -100, label.y = 400,
inherit.aes = F, color = "#F8766d", size = 5)## Warning: Removed 11 rows containing non-finite values (stat_smooth).## Warning: Removed 7 rows containing non-finite values (stat_smooth).## Warning: Removed 11 rows containing non-finite values (stat_cor).## Warning: Removed 7 rows containing non-finite values (stat_cor).## Warning: Removed 18 rows containing missing values (geom_point).
#+
#geom_text(aes(x = 20, y = 120, label = paste("rho =", "-0.76**")),color = "red",size=4)
ggsave("bucld_ssl_tsl_rt_corr.png",
width = 15, height = 15, units = "cm")## Warning: Removed 11 rows containing non-finite values (stat_smooth).## Warning: Removed 7 rows containing non-finite values (stat_smooth).## Warning: Removed 11 rows containing non-finite values (stat_cor).## Warning: Removed 7 rows containing non-finite values (stat_cor).## Warning: Removed 18 rows containing missing values (geom_point).Behavioral Correlations
Heat Map
Heat Map for R, P, N for behavioral correlations (TD + ASD)
Within Task, Across Measures
Across Behavioral Tasks, Within Measure (TD + ASD)
Across Behavioral Tasks, Within Measure (TD or ASD Only)
library("ggplot2")
plot_heatmap_r_func <-
function(df) {
ggplot(data = df, aes(x = column, y = row, fill = cor)) +
geom_tile(color = "white") +
scale_fill_gradient2(
low = "blue",
high = "red",
mid = "white",
midpoint = 0,
limit = c(-1, 1),
space = "Lab",
name = "pearson\nCorrelation r"
) +
theme_minimal() +
theme(axis.text.x = element_text(
angle = 90,
vjust = 1,
size = 10,
hjust = 1
)) +
theme(axis.title.x = element_blank(),
axis.text.y = element_text (size = 10),
axis.title.y = element_blank()) +
coord_fixed() +
geom_text(aes(label = cor), color = "black", size = 2)
}
plot_heatmap_p_func <-
function(df) {
ggplot(data = heat_map_corr_data_task, aes(x = column, y = row, fill = p)) +
geom_tile(color = "white") +
scale_fill_gradient2(
low = "blue",
high = "red",
mid = "white",
midpoint = 0,
limit = c(0, 1),
space = "Lab",
name = "p-value"
) +
theme_minimal() +
theme(axis.text.x = element_text(
angle = 90,
vjust = 1,
size = 10,
hjust = 1
)) +
theme(axis.title.x = element_blank(),
axis.text.y = element_text (size = 10),
axis.title.y = element_blank()) +
coord_fixed() +
geom_text(aes(label = p), color = "black", size = 2)
}
plot_heatmap_r_func(heat_map_corr_data_task_td)## Warning: Removed 44 rows containing missing values (geom_text).
ggsave(
"corr_within_measure_td.png",
dpi = 150
)## Saving 7 x 5 in image## Warning: Removed 44 rows containing missing values (geom_text).plot_heatmap_r_func(heat_map_corr_data_td)## Warning: Removed 44 rows containing missing values (geom_text).
ggsave(
"corr_within_task_td.png",
dpi = 150)## Saving 7 x 5 in image## Warning: Removed 44 rows containing missing values (geom_text).plot_heatmap_r_func(heat_map_corr_data_asd)
ggsave(
"corr_within_task_asd.png",
dpi = 150)## Saving 7 x 5 in imageplot_heatmap_r_func(heat_map_corr_data_task_asd)
ggsave(
"corr_within_measure_asd.png",
dpi = 150
)## Saving 7 x 5 in imageplot_heatmap_r_func(heat_map_corr_data_task_asd)
ggsave(
"corr_within_measure_asd.png",
dpi = 150)## Saving 7 x 5 in imageplot_heatmap_p_func(heat_map_corr_data_task_td)
ggsave(
"corr_p_within_measure_td.png",
dpi = 150
)## Saving 7 x 5 in imageplot_heatmap_p_func(heat_map_corr_data_task_asd)
ggsave(
"corr_p_within_measure_asd.png",
dpi = 150
)## Saving 7 x 5 in imageplot_heatmap_p_func(heat_map_corr_data_td)
ggsave(
"corr_p_within_task_td.png",
dpi = 150
)## Saving 7 x 5 in imageplot_heatmap_p_func(heat_map_corr_data_asd)
ggsave(
"corr_p_within_task_asd.png",
dpi = 150
)## Saving 7 x 5 in imageIn Progress, Ignore below
Beh Corr Plots
Ignore: ASD Beh Corr Plot